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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-12 06:28:41
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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4.05k
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swlee6/gpt-oss-20b-multilingual-reasoner
|
swlee6
| 2025-08-11T09:12:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"dataset:HuggingFaceH4/Multilingual-Thinking",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T08:51:43Z |
---
base_model: openai/gpt-oss-20b
datasets: HuggingFaceH4/Multilingual-Thinking
library_name: transformers
model_name: gpt-oss-20b-multilingual-reasoner
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gpt-oss-20b-multilingual-reasoner
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [HuggingFaceH4/Multilingual-Thinking](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="swlee6/gpt-oss-20b-multilingual-reasoner", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
winnieyangwannan/entity_sft_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_1280_all_37_epoch_1_layer_all
|
winnieyangwannan
| 2025-08-11T09:12:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T08:03:24Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
tumini21/blockassist-bc-hairy_howling_butterfly_1754903389
|
tumini21
| 2025-08-11T09:10:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy howling butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:10:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy howling butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
HaseebAsif/rl_course_vizdoom_health_gathering_supreme
|
HaseebAsif
| 2025-08-11T09:08:54Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-11T09:08:47Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.35 +/- 5.35
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r HaseebAsif/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
michaelcpage345/blockassist-bc-miniature_deadly_anteater_1754901364
|
michaelcpage345
| 2025-08-11T09:05:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature deadly anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:05:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature deadly anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
raniero/instruction_local_test
|
raniero
| 2025-08-11T09:05:25Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-08-11T07:43:02Z |
# LoRA — Instruction SFT
- **Task ID:** instruction-lora-test6
- **Base model:** mistralai/Mistral-7B-Instruct-v0.2
- **SHA256 (adapter):** `27a54e2d5710ae2ee0660e485c0dfc9e6d24b712cc3fc2f72b6f570e8eb4d433`
- **Repo:** raniero/instruction_local_test
Questa repo contiene SOLO gli adapter LoRA richiesti dai validator Subnet 56.
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754902866
|
ggozzy
| 2025-08-11T09:03:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:02:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1754902858
|
roeker
| 2025-08-11T09:02:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:01:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1754902803
|
lqpl
| 2025-08-11T09:01:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T09:00:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bisectgroup/FGR
|
bisectgroup
| 2025-08-11T08:55:44Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T08:26:44Z |
---
license: apache-2.0
---
|
thegreatgame/exaone-accounting-fore-lora
|
thegreatgame
| 2025-08-11T08:48:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T08:48:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
blocksync/blockassist-bc-pouncing_bristly_finch_1754891344
|
blocksync
| 2025-08-11T08:47:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pouncing bristly finch",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:46:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pouncing bristly finch
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1754901748
|
roeker
| 2025-08-11T08:43:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:43:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Suprim003/Reinforce-CartPole-v1
|
Suprim003
| 2025-08-11T08:41:51Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-11T08:41:41Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 460.90 +/- 117.30
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
FrAnKu34t23/Test
|
FrAnKu34t23
| 2025-08-11T08:40:31Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:distilgpt2",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:distilbert/distilgpt2",
"base_model:adapter:distilbert/distilgpt2",
"region:us"
] |
text-generation
| 2025-08-11T08:40:27Z |
---
base_model: distilgpt2
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:distilgpt2
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
sii-research/DigitalGene-32B
|
sii-research
| 2025-08-11T08:40:16Z | 0 | 0 | null |
[
"safetensors",
"qwen2_5_vl",
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T08:10:49Z |
---
license: apache-2.0
---
|
esi777/blockassist-bc-camouflaged_trotting_eel_1754901434
|
esi777
| 2025-08-11T08:37:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged trotting eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:37:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged trotting eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Stew-Dude/distilbert-base-uncased-finetuned-emotion
|
Stew-Dude
| 2025-08-11T08:35:52Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T08:35:37Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2238
- Accuracy: 0.9255
- F1: 0.9252
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8385 | 1.0 | 250 | 0.3377 | 0.9005 | 0.8987 |
| 0.2591 | 2.0 | 500 | 0.2238 | 0.9255 | 0.9252 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
AXERA-TECH/satrn
|
AXERA-TECH
| 2025-08-11T08:35:13Z | 3 | 0 | null |
[
"onnx",
"Transformer",
"ONNX",
"ocr",
"mmocr",
"satrn",
"en",
"license:bsd-3-clause-clear",
"region:us"
] | null | 2025-06-11T03:08:44Z |
---
license: bsd-3-clause-clear
language:
- en
tags:
- Transformer
- ONNX
- ocr
- mmocr
- satrn
---
# satrn
[original repo](https://github.com/open-mmlab/mmocr/blob/main/configs/textrecog/satrn/README.md)
## Convert tools links:
For those who are interested in model conversion, you can try to export onnx or axmodel through
[satrn.axera](https://github.com/AXERA-TECH/satrn.axera)
## Installation
```
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
git clone https://github.com/open-mmlab/mmocr.git
cd mmocr
mim install -e .
```
## Support Platform
- AX650
- [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
The speed measurements(under different NPU configurations ) of the two parts of SATRN:
(1) backbone+encoder
(2) decoder
||backbone+encoder(ms)|decoder(ms)|
|--|--|--|
|NPU1|20.494|2.648|
|NPU2|9.785|1.504|
|NPU3|6.085|1.384|
## How to use
Download all files from this repository to the device
```
.
├── axmodel
│ ├── backbone_encoder.axmodel
│ └── decoder.axmodel
├── demo_text_recog.jpg
├── onnx
│ ├── satrn_backbone_encoder.onnx
│ └── satrn_decoder_sim.onnx
├── README.md
├── run_axmodel.py
├── run_model.py
└── run_onnx.py
```
### python env requirement
#### 1. pyaxengine
https://github.com/AXERA-TECH/pyaxengine
```
wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.1rc0/axengine-0.1.1-py3-none-any.whl
pip install axengine-0.1.1-py3-none-any.whl
```
#### 2. satrn
[satrn installation](https://github.com/open-mmlab/mmocr/tree/main?tab=readme-ov-file#installation)
#### Inference onnxmodel
```
python run_onnx.py
```
input:
output:
```
pred_text: STAR
score: [0.9384028315544128, 0.9574984908103943, 0.9993689656257629, 0.9994958639144897]
```
#### Inference with AX650 Host
check the [reference](https://github.com/AXERA-TECH/satrn.axera) for more information
|
Hfkjc/blockassist-bc-fanged_stinging_sandpiper_1754900848
|
Hfkjc
| 2025-08-11T08:34:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fanged stinging sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:34:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fanged stinging sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tamewild/4b_v43_merged_e5
|
tamewild
| 2025-08-11T08:34:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T08:32:15Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
roeker/blockassist-bc-quick_wiry_owl_1754901009
|
roeker
| 2025-08-11T08:31:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:31:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tamewild/4b_v43_merged_e10
|
tamewild
| 2025-08-11T08:28:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T08:26:12Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
shiimi/wav2vec2
|
shiimi
| 2025-08-11T08:28:34Z | 0 | 0 | null |
[
"pytorch",
"wav2vec2",
"generated_from_trainer",
"dataset:common_voice_17_0",
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T07:41:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
model-index:
- name: wav2vec2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_17_0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.13.3
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754900713
|
ggozzy
| 2025-08-11T08:26:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:26:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
EnriqueSolarte/qwen2-7b-instruct-amazon-description
|
EnriqueSolarte
| 2025-08-11T08:25:21Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T08:05:19Z |
---
base_model: Qwen/Qwen2-VL-7B-Instruct
library_name: transformers
model_name: qwen2-7b-instruct-amazon-description
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2-7b-instruct-amazon-description
This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="EnriqueSolarte/qwen2-7b-instruct-amazon-description", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
BinBashir/TinyBERT_on_jumia_dataset
|
BinBashir
| 2025-08-11T08:19:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T08:19:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1754899201
|
Sayemahsjn
| 2025-08-11T08:18:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:17:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ChickenMcSwag/gpt-oss-20b-lora-finetuned_fp4_step_40
|
ChickenMcSwag
| 2025-08-11T08:17:46Z | 0 | 0 | null |
[
"safetensors",
"gpt_oss",
"gpt-oss-20b",
"lora",
"merged",
"causal-lm",
"en",
"base_model:openai/gpt-oss-20b",
"base_model:adapter:openai/gpt-oss-20b",
"license:other",
"region:us"
] | null | 2025-08-11T06:20:53Z |
---
license: other
base_model: openai/gpt-oss-20b
tags:
- gpt-oss-20b
- lora
- merged
- causal-lm
language:
- en
---
# gpt-oss-20b-lora-finetuned_fp4_step_40
This is a merged model combining GPT-OSS-20B with a fine-tuned LoRA adapter.
## Model Details
- **Base Model**: openai/gpt-oss-20b
- **LoRA Checkpoint**: checkpoint-40
- **Model Type**: Causal Language Model
- **Model Size**: ~20B parameters
- **Tensor Type**: bfloat16
## LoRA Configuration
- **Rank (r)**: 8
- **Alpha**: 16
- **Target Modules**: k_proj, v_proj, o_proj, q_proj
- **Special MLP Expert Layers**: Layers 7, 15, 23
## Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
"ChickenMcSwag/gpt-oss-20b-lora-finetuned_fp4_step_40",
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("ChickenMcSwag/gpt-oss-20b-lora-finetuned_fp4_step_40")
# Generate text
prompt = "The future of AI is"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=100,
temperature=0.7,
do_sample=True,
top_p=0.95
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Hardware Requirements
- **Minimum VRAM**: ~40GB for inference
- **Recommended**: 2x A100 80GB or equivalent
## License
This model follows the original GPT-OSS-20B license. Please refer to the base model's license and usage policy.
## Citation
If you use this model, please cite the original GPT-OSS-20B model.
|
vengky/blockassist-bc-wild_gentle_manatee_1754897781
|
vengky
| 2025-08-11T08:17:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild gentle manatee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:16:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild gentle manatee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zhpphh/bert-finetuned-ner
|
zhpphh
| 2025-08-11T08:16:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-11T07:58:57Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9346567411083541
- name: Recall
type: recall
value: 0.9508582968697409
- name: F1
type: f1
value: 0.9426879119045634
- name: Accuracy
type: accuracy
value: 0.9864602342968152
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0609
- Precision: 0.9347
- Recall: 0.9509
- F1: 0.9427
- Accuracy: 0.9865
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0755 | 1.0 | 1756 | 0.0741 | 0.8994 | 0.9310 | 0.9149 | 0.9804 |
| 0.0343 | 2.0 | 3512 | 0.0704 | 0.9318 | 0.9450 | 0.9383 | 0.9851 |
| 0.0198 | 3.0 | 5268 | 0.0609 | 0.9347 | 0.9509 | 0.9427 | 0.9865 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 2.14.6
- Tokenizers 0.21.4
|
bapi2025/blockassist-bc-lanky_silky_duck_1754898598
|
bapi2025
| 2025-08-11T08:15:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lanky silky duck",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:14:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lanky silky duck
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Salesforce/moirai-2.0-R-small
|
Salesforce
| 2025-08-11T08:15:26Z | 360 | 4 | null |
[
"safetensors",
"time series",
"forecasting",
"pretrained models",
"foundation models",
"time series foundation models",
"time-series",
"time-series-forecasting",
"arxiv:2403.07815",
"arxiv:2402.02592",
"license:cc-by-nc-4.0",
"region:us"
] |
time-series-forecasting
| 2025-08-06T14:03:58Z |
---
license: cc-by-nc-4.0
pipeline_tag: time-series-forecasting
tags:
- time series
- forecasting
- pretrained models
- foundation models
- time series foundation models
- time-series
---
# Moirai-2.0-R-Small
Moirai 2.0 is a decoder-only universal time series forecasting transformer model pre-trained on:
- Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context).
- Mixup data generated from non-leaking subsets of [Chronos Dataset](https://arxiv.org/abs/2403.07815).
- Synthetic time series produced via KernelSynth introduced in [Chronos paper](https://arxiv.org/abs/2403.07815).
- Internal Salesforce operational data.
We make significant improvements over the first version of Moirai (please refer to the [paper](https://arxiv.org/abs/2402.02592) for previous version):
- Switched from a distributional loss to a quantile loss formulation.
- Moved from single-token to multi-token prediction, improving efficiency and stability.
- Added a data filtering mechanism to filter out non-forecastable, low quality, time series during pretraining.
- Added a new patch token embedding which includes missing value information.
- Added patch-level random mask to improve robustness of the model during inference.
## Usage
To perform inference with Moirai 2.0, install the uni2ts library from our [GitHub repo](https://github.com/SalesforceAIResearch/uni2ts).
1. Clone repository:
```shell
git clone https://github.com/SalesforceAIResearch/uni2ts.git
cd uni2ts
```
2) Create virtual environment:
```shell
virtualenv venv
. venv/bin/activate
```
3) Build from source:
```shell
pip install -e '.[notebook]'
```
4) Create a `.env` file:
```shell
touch .env
```
A simple notebook to get started: [github_notebook_link](https://github.com/SalesforceAIResearch/uni2ts/blob/main/example/moirai_forecast.ipynb)
## Citation
If you're using any Moirai model or Uni2TS in your research or applications, please cite it using this BibTeX:
```markdown
@article{woo2024unified,
title={Unified Training of Universal Time Series Forecasting Transformers},
author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Xiong, Caiming and Savarese, Silvio and Sahoo, Doyen},
journal={arXiv preprint arXiv:2402.02592},
year={2024}
}
```
## Ethical Considerations
This release is for research purposes only in support of an academic paper.
Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes.
We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model.
We encourage users to consider the common limitations of AI, comply with applicable laws,
and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly
impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
|
winnieyangwannan/entity_sft_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_12800_all_37_epoch_1_layer_all
|
winnieyangwannan
| 2025-08-11T08:08:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T08:03:20Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
roeker/blockassist-bc-quick_wiry_owl_1754899536
|
roeker
| 2025-08-11T08:07:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:06:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
winnieyangwannan/entity_sft_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_3840_all_37_epoch_1_layer_all
|
winnieyangwannan
| 2025-08-11T08:06:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T08:03:25Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
kayacrypto/blockassist-bc-thriving_barky_wolf_1754899271
|
kayacrypto
| 2025-08-11T08:04:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving barky wolf",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:03:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving barky wolf
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754899145
|
IvanJAjebu
| 2025-08-11T08:00:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T08:00:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754898983
|
ggozzy
| 2025-08-11T07:57:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:57:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
acidjp/blockassist-bc-pesty_extinct_prawn_1754898516
|
acidjp
| 2025-08-11T07:56:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:55:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754898790
|
IvanJAjebu
| 2025-08-11T07:54:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:54:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ykhushbu183/blockassist-bc-singing_wild_cod_1754898616
|
ykhushbu183
| 2025-08-11T07:52:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing wild cod",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:52:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing wild cod
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SNUMPR/Terran-c
|
SNUMPR
| 2025-08-11T07:51:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2025-08-11T07:37:04Z |
---
language:
- en
library_name: transformers
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.51.3
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="SNUMPR/Terran-c",
torch_dtype="auto",
trust_remote_code=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 4096
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
res = generate_text(
messages,
renormalize_logits=True
)
print(res[0]["generated_text"][-1]['content'])
```
You can print a sample prompt after applying chat template to see how it is feed to the tokenizer:
```python
print(generate_text.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
))
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "SNUMPR/Terran-c" # either local folder or Hugging Face model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 4096
# model.generation_config.do_sample = False
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.0)
# model.generation_config.repetition_penalty = float(1.0)
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
Qwen3ForCausalLM(
(model): Qwen3Model(
(embed_tokens): Embedding(151936, 2048, padding_idx=151643)
(layers): ModuleList(
(0-27): 28 x Qwen3DecoderLayer(
(self_attn): Qwen3Attention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=1024, bias=False)
(v_proj): Linear(in_features=2048, out_features=1024, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(q_norm): Qwen3RMSNorm((128,), eps=1e-06)
(k_norm): Qwen3RMSNorm((128,), eps=1e-06)
)
(mlp): Qwen3MLP(
(gate_proj): Linear(in_features=2048, out_features=6144, bias=False)
(up_proj): Linear(in_features=2048, out_features=6144, bias=False)
(down_proj): Linear(in_features=6144, out_features=2048, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Qwen3RMSNorm((2048,), eps=1e-06)
(post_attention_layernorm): Qwen3RMSNorm((2048,), eps=1e-06)
)
)
(norm): Qwen3RMSNorm((2048,), eps=1e-06)
(rotary_emb): Qwen3RotaryEmbedding()
)
(lm_head): Linear(in_features=2048, out_features=151936, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
RMCian/blockassist-bc-wiry_sturdy_cobra_1754898618
|
RMCian
| 2025-08-11T07:50:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry sturdy cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:50:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry sturdy cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ChickenMcSwag/gpt-oss-20b-lora-finetuned_fp4_step_20
|
ChickenMcSwag
| 2025-08-11T07:49:42Z | 0 | 0 | null |
[
"safetensors",
"gpt_oss",
"gpt-oss-20b",
"lora",
"merged",
"causal-lm",
"en",
"base_model:openai/gpt-oss-20b",
"base_model:adapter:openai/gpt-oss-20b",
"license:other",
"region:us"
] | null | 2025-08-11T06:20:07Z |
---
license: other
base_model: openai/gpt-oss-20b
tags:
- gpt-oss-20b
- lora
- merged
- causal-lm
language:
- en
---
# gpt-oss-20b-lora-finetuned_fp4_step_20
This is a merged model combining GPT-OSS-20B with a fine-tuned LoRA adapter.
## Model Details
- **Base Model**: openai/gpt-oss-20b
- **LoRA Checkpoint**: checkpoint-20
- **Model Type**: Causal Language Model
- **Model Size**: ~20B parameters
- **Tensor Type**: bfloat16
## LoRA Configuration
- **Rank (r)**: 8
- **Alpha**: 16
- **Target Modules**: k_proj, v_proj, o_proj, q_proj
- **Special MLP Expert Layers**: Layers 7, 15, 23
## Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
"ChickenMcSwag/gpt-oss-20b-lora-finetuned_fp4_step_20",
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("ChickenMcSwag/gpt-oss-20b-lora-finetuned_fp4_step_20")
# Generate text
prompt = "The future of AI is"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_length=100,
temperature=0.7,
do_sample=True,
top_p=0.95
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Hardware Requirements
- **Minimum VRAM**: ~40GB for inference
- **Recommended**: 2x A100 80GB or equivalent
## License
This model follows the original GPT-OSS-20B license. Please refer to the base model's license and usage policy.
## Citation
If you use this model, please cite the original GPT-OSS-20B model.
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754898449
|
IvanJAjebu
| 2025-08-11T07:48:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:48:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1754898420
|
roeker
| 2025-08-11T07:47:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:47:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
neuphonic/distill-neucodec
|
neuphonic
| 2025-08-11T07:47:13Z | 0 | 3 | null |
[
"pytorch",
"audio",
"speech",
"audio-to-audio",
"speech-language-models",
"dataset:amphion/Emilia-Dataset",
"dataset:facebook/multilingual_librispeech",
"dataset:CSTR-Edinburgh/vctk",
"dataset:google/fleurs",
"dataset:mozilla-foundation/common_voice_13_0",
"dataset:mythicinfinity/libritts_r",
"arxiv:2409.05377",
"arxiv:2504.04949",
"license:apache-2.0",
"region:us"
] |
audio-to-audio
| 2025-08-06T13:48:19Z |
---
license: apache-2.0
tags:
- audio
- speech
- audio-to-audio
- speech-language-models
datasets:
- amphion/Emilia-Dataset
- facebook/multilingual_librispeech
- CSTR-Edinburgh/vctk
- google/fleurs
- mozilla-foundation/common_voice_13_0
- mythicinfinity/libritts_r
---
# Model Details
Distill-NeuCodec is a version of NeuCodec with a compatible, distilled encoder.
The distilled encoder is 10x smaller in parameter count and uses ~7.5x less MACs at inference time.
The distilled model makes the following adjustments to the model:
* Swap the notoriuously slow [BigCodec](https://arxiv.org/abs/2409.05377) acoustic encoder for the [SQCodec](https://arxiv.org/abs/2504.04949) acoustic encoder (70m → 36m)
* Swap the [w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) semantic encoder for [DistilHuBERT](https://huggingface.co/ntu-spml/distilhubert) (600m → 21m)
Our work is largely based on extending the work of [X-Codec2.0](https://huggingface.co/HKUSTAudio/xcodec2) and [SQCodec](https://arxiv.org/abs/2504.04949).
- **Developed by:** Neuphonic
- **Model type:** Neural Audio Codec
- **License:** apache-2.0
- **Repository:** https://github.com/neuphonic/neucodec
- **Paper:** *Coming soon!*
- **Pre-encoded Datasets:** *Coming soon!*
## Get Started
Use the code below to get started with the model.
To install from pypi in a dedicated environment, using Python 3.10 or above:
```bash
conda create -n neucodec python=3.10
conda activate neucodec
pip install neucodec
```
Then, to use in python:
```python
import librosa
import torch
import torchaudio
from torchaudio import transforms as T
from neucodec import DistillNeuCodec
model = DistillNeuCodec.from_pretrained("neuphonic/distill-neucodec")
model.eval().cuda()
y, sr = torchaudio.load(librosa.ex("libri1"))
if sr != 16_000:
y = T.Resample(sr, 16_000)(y)[None, ...] # (B, 1, T_16)
with torch.no_grad():
fsq_codes = model.encode_code(y)
# fsq_codes = model.encode_code(librosa.ex("libri1")) # or directly pass your filepath!
print(f"Codes shape: {fsq_codes.shape}")
recon = model.decode_code(fsq_codes).cpu() # (B, 1, T_24)
torchaudio.save("reconstructed.wav", recon[0, :, :], 24_000)
```
## Training Details
The model was trained using the same data as the full model, with an additional distillation loss (MSE between distilled and original encoder ouputs).
|
SNUMPR/Zerg-b
|
SNUMPR
| 2025-08-11T07:45:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2025-08-11T07:36:12Z |
---
language:
- en
library_name: transformers
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.51.3
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="SNUMPR/Zerg-b",
torch_dtype="auto",
trust_remote_code=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 4096
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
res = generate_text(
messages,
renormalize_logits=True
)
print(res[0]["generated_text"][-1]['content'])
```
You can print a sample prompt after applying chat template to see how it is feed to the tokenizer:
```python
print(generate_text.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
))
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "SNUMPR/Zerg-b" # either local folder or Hugging Face model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 4096
# model.generation_config.do_sample = False
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.0)
# model.generation_config.repetition_penalty = float(1.0)
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
Qwen3ForCausalLM(
(model): Qwen3Model(
(embed_tokens): Embedding(151936, 2048, padding_idx=151643)
(layers): ModuleList(
(0-27): 28 x Qwen3DecoderLayer(
(self_attn): Qwen3Attention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=1024, bias=False)
(v_proj): Linear(in_features=2048, out_features=1024, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(q_norm): Qwen3RMSNorm((128,), eps=1e-06)
(k_norm): Qwen3RMSNorm((128,), eps=1e-06)
)
(mlp): Qwen3MLP(
(gate_proj): Linear(in_features=2048, out_features=6144, bias=False)
(up_proj): Linear(in_features=2048, out_features=6144, bias=False)
(down_proj): Linear(in_features=6144, out_features=2048, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Qwen3RMSNorm((2048,), eps=1e-06)
(post_attention_layernorm): Qwen3RMSNorm((2048,), eps=1e-06)
)
)
(norm): Qwen3RMSNorm((2048,), eps=1e-06)
(rotary_emb): Qwen3RotaryEmbedding()
)
(lm_head): Linear(in_features=2048, out_features=151936, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
RMCian/blockassist-bc-wiry_sturdy_cobra_1754898209
|
RMCian
| 2025-08-11T07:43:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry sturdy cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:43:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry sturdy cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
2hpsatt/blockassist-bc-huge_deft_eagle_1754898129
|
2hpsatt
| 2025-08-11T07:43:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:43:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aXsalll/blockassist-bc-chattering_galloping_ape_1754898085
|
aXsalll
| 2025-08-11T07:43:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"chattering galloping ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:42:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- chattering galloping ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kumoooo/blockassist-bc-aquatic_restless_camel_1754897062
|
kumoooo
| 2025-08-11T07:34:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic restless camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:33:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic restless camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754897545
|
IvanJAjebu
| 2025-08-11T07:33:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:33:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SNUMPR/Protoss-b
|
SNUMPR
| 2025-08-11T07:33:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2025-08-11T07:23:07Z |
---
language:
- en
library_name: transformers
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.51.3
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="SNUMPR/Protoss-b",
torch_dtype="auto",
trust_remote_code=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 4096
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
res = generate_text(
messages,
renormalize_logits=True
)
print(res[0]["generated_text"][-1]['content'])
```
You can print a sample prompt after applying chat template to see how it is feed to the tokenizer:
```python
print(generate_text.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
))
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "SNUMPR/Protoss-b" # either local folder or Hugging Face model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 4096
# model.generation_config.do_sample = False
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.0)
# model.generation_config.repetition_penalty = float(1.0)
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
Qwen3ForCausalLM(
(model): Qwen3Model(
(embed_tokens): Embedding(151936, 2048, padding_idx=151643)
(layers): ModuleList(
(0-27): 28 x Qwen3DecoderLayer(
(self_attn): Qwen3Attention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=1024, bias=False)
(v_proj): Linear(in_features=2048, out_features=1024, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(q_norm): Qwen3RMSNorm((128,), eps=1e-06)
(k_norm): Qwen3RMSNorm((128,), eps=1e-06)
)
(mlp): Qwen3MLP(
(gate_proj): Linear(in_features=2048, out_features=6144, bias=False)
(up_proj): Linear(in_features=2048, out_features=6144, bias=False)
(down_proj): Linear(in_features=6144, out_features=2048, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Qwen3RMSNorm((2048,), eps=1e-06)
(post_attention_layernorm): Qwen3RMSNorm((2048,), eps=1e-06)
)
)
(norm): Qwen3RMSNorm((2048,), eps=1e-06)
(rotary_emb): Qwen3RotaryEmbedding()
)
(lm_head): Linear(in_features=2048, out_features=151936, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
SNUMPR/Protoss-c
|
SNUMPR
| 2025-08-11T07:32:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2025-08-11T07:27:49Z |
---
language:
- en
library_name: transformers
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.51.3
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="SNUMPR/Protoss-c",
torch_dtype="auto",
trust_remote_code=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 4096
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
res = generate_text(
messages,
renormalize_logits=True
)
print(res[0]["generated_text"][-1]['content'])
```
You can print a sample prompt after applying chat template to see how it is feed to the tokenizer:
```python
print(generate_text.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
))
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "SNUMPR/Protoss-c" # either local folder or Hugging Face model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 4096
# model.generation_config.do_sample = False
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.0)
# model.generation_config.repetition_penalty = float(1.0)
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
Qwen3ForCausalLM(
(model): Qwen3Model(
(embed_tokens): Embedding(151936, 2048, padding_idx=151643)
(layers): ModuleList(
(0-27): 28 x Qwen3DecoderLayer(
(self_attn): Qwen3Attention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=1024, bias=False)
(v_proj): Linear(in_features=2048, out_features=1024, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(q_norm): Qwen3RMSNorm((128,), eps=1e-06)
(k_norm): Qwen3RMSNorm((128,), eps=1e-06)
)
(mlp): Qwen3MLP(
(gate_proj): Linear(in_features=2048, out_features=6144, bias=False)
(up_proj): Linear(in_features=2048, out_features=6144, bias=False)
(down_proj): Linear(in_features=6144, out_features=2048, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Qwen3RMSNorm((2048,), eps=1e-06)
(post_attention_layernorm): Qwen3RMSNorm((2048,), eps=1e-06)
)
)
(norm): Qwen3RMSNorm((2048,), eps=1e-06)
(rotary_emb): Qwen3RotaryEmbedding()
)
(lm_head): Linear(in_features=2048, out_features=151936, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
nilli2038/blockassist-bc-gentle_gregarious_mouse_1754897383
|
nilli2038
| 2025-08-11T07:32:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle gregarious mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:31:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle gregarious mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Felipydias/Fotos
|
Felipydias
| 2025-08-11T07:26:21Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T07:26:20Z |
---
license: apache-2.0
---
|
roeker/blockassist-bc-quick_wiry_owl_1754896940
|
roeker
| 2025-08-11T07:23:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:23:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Soughing/tpa_xxl
|
Soughing
| 2025-08-11T07:16:23Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T07:16:23Z |
---
license: apache-2.0
---
|
Soughing/gqa_xxl
|
Soughing
| 2025-08-11T07:15:03Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T07:15:03Z |
---
license: apache-2.0
---
|
Soughing/mqa_xxl
|
Soughing
| 2025-08-11T07:14:33Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T07:14:33Z |
---
license: apache-2.0
---
|
kathleenkk23/distilbert-base-uncased-finetuned-cola
|
kathleenkk23
| 2025-08-11T07:14:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T05:42:12Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0345
- Accuracy: 0.8130
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1168 | 1.0 | 535 | 0.7213 | 0.7795 |
| 0.2222 | 2.0 | 1070 | 0.6551 | 0.8054 |
| 0.1564 | 3.0 | 1605 | 0.8472 | 0.8082 |
| 0.1055 | 4.0 | 2140 | 1.0309 | 0.8082 |
| 0.0793 | 5.0 | 2675 | 1.0345 | 0.8130 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.7.1+cu118
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Rachmaninofffff/my_awesome_billsum_model
|
Rachmaninofffff
| 2025-08-11T07:10:58Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T06:27:23Z |
---
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4850
- Rouge1: 0.1516
- Rouge2: 0.0525
- Rougel: 0.1235
- Rougelsum: 0.1233
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 4.6025 | 0.0323 | 2 | 4.3905 | 0.1451 | 0.0493 | 0.1214 | 0.1215 | 20.0 |
| 4.6391 | 0.0645 | 4 | 4.1843 | 0.1445 | 0.0485 | 0.1205 | 0.1205 | 20.0 |
| 4.6134 | 0.0968 | 6 | 4.0432 | 0.1452 | 0.0485 | 0.1203 | 0.1204 | 20.0 |
| 4.524 | 0.1290 | 8 | 3.9155 | 0.1449 | 0.0479 | 0.1202 | 0.1202 | 20.0 |
| 4.316 | 0.1613 | 10 | 3.7016 | 0.1457 | 0.0489 | 0.1209 | 0.1209 | 20.0 |
| 3.8839 | 0.1935 | 12 | 3.6427 | 0.1453 | 0.048 | 0.1207 | 0.1207 | 20.0 |
| 3.7073 | 0.2258 | 14 | 3.5389 | 0.143 | 0.0471 | 0.1191 | 0.1191 | 20.0 |
| 4.0213 | 0.2581 | 16 | 3.4424 | 0.1413 | 0.0449 | 0.1176 | 0.1176 | 20.0 |
| 3.6408 | 0.2903 | 18 | 3.3590 | 0.1417 | 0.0453 | 0.1177 | 0.1177 | 20.0 |
| 3.5079 | 0.3226 | 20 | 3.2958 | 0.142 | 0.0454 | 0.1179 | 0.1179 | 20.0 |
| 3.5459 | 0.3548 | 22 | 3.2398 | 0.1404 | 0.0442 | 0.1162 | 0.1161 | 20.0 |
| 3.6465 | 0.3871 | 24 | 3.1787 | 0.1399 | 0.0435 | 0.1158 | 0.1156 | 20.0 |
| 3.7268 | 0.4194 | 26 | 3.1386 | 0.1385 | 0.0424 | 0.1148 | 0.1146 | 20.0 |
| 3.4255 | 0.4516 | 28 | 3.1048 | 0.1384 | 0.0418 | 0.1149 | 0.1147 | 20.0 |
| 3.4005 | 0.4839 | 30 | 3.0698 | 0.1377 | 0.0414 | 0.1143 | 0.1141 | 20.0 |
| 3.2091 | 0.5161 | 32 | 3.0406 | 0.1372 | 0.0416 | 0.1135 | 0.1133 | 20.0 |
| 3.1051 | 0.5484 | 34 | 3.0139 | 0.1372 | 0.042 | 0.1138 | 0.1135 | 20.0 |
| 3.2501 | 0.5806 | 36 | 2.9853 | 0.1361 | 0.0407 | 0.1124 | 0.1121 | 20.0 |
| 3.163 | 0.6129 | 38 | 2.9594 | 0.1353 | 0.04 | 0.1117 | 0.1115 | 20.0 |
| 3.2925 | 0.6452 | 40 | 2.9367 | 0.1351 | 0.0408 | 0.1117 | 0.1114 | 20.0 |
| 3.2167 | 0.6774 | 42 | 2.9150 | 0.1337 | 0.0393 | 0.1108 | 0.1107 | 20.0 |
| 3.0087 | 0.7097 | 44 | 2.8946 | 0.1336 | 0.0395 | 0.1105 | 0.1104 | 20.0 |
| 3.1278 | 0.7419 | 46 | 2.8756 | 0.133 | 0.0395 | 0.1102 | 0.1102 | 20.0 |
| 3.0755 | 0.7742 | 48 | 2.8578 | 0.1333 | 0.0397 | 0.1106 | 0.1105 | 20.0 |
| 3.2294 | 0.8065 | 50 | 2.8412 | 0.1335 | 0.0394 | 0.1107 | 0.1105 | 20.0 |
| 3.0096 | 0.8387 | 52 | 2.8254 | 0.1334 | 0.039 | 0.1105 | 0.1105 | 20.0 |
| 3.0859 | 0.8710 | 54 | 2.8103 | 0.1325 | 0.039 | 0.1106 | 0.1105 | 20.0 |
| 2.9677 | 0.9032 | 56 | 2.7963 | 0.1325 | 0.0392 | 0.11 | 0.1098 | 20.0 |
| 3.0279 | 0.9355 | 58 | 2.7832 | 0.1311 | 0.0382 | 0.1091 | 0.1091 | 20.0 |
| 3.2149 | 0.9677 | 60 | 2.7704 | 0.13 | 0.0373 | 0.1081 | 0.108 | 20.0 |
| 2.9505 | 1.0 | 62 | 2.7582 | 0.1295 | 0.0358 | 0.1068 | 0.1066 | 20.0 |
| 2.9576 | 1.0323 | 64 | 2.7467 | 0.1312 | 0.0381 | 0.1084 | 0.1082 | 20.0 |
| 2.8689 | 1.0645 | 66 | 2.7359 | 0.1302 | 0.0372 | 0.1075 | 0.1074 | 20.0 |
| 2.9004 | 1.0968 | 68 | 2.7256 | 0.1302 | 0.0381 | 0.1073 | 0.1072 | 20.0 |
| 3.1511 | 1.1290 | 70 | 2.7158 | 0.1311 | 0.0389 | 0.1078 | 0.1076 | 20.0 |
| 3.0243 | 1.1613 | 72 | 2.7064 | 0.1317 | 0.0394 | 0.1088 | 0.1087 | 20.0 |
| 3.0784 | 1.1935 | 74 | 2.6971 | 0.1328 | 0.0404 | 0.1098 | 0.1097 | 20.0 |
| 2.9897 | 1.2258 | 76 | 2.6884 | 0.1338 | 0.0414 | 0.1106 | 0.1105 | 20.0 |
| 2.7283 | 1.2581 | 78 | 2.6803 | 0.1331 | 0.0409 | 0.11 | 0.1099 | 20.0 |
| 3.021 | 1.2903 | 80 | 2.6724 | 0.1345 | 0.0421 | 0.111 | 0.1108 | 20.0 |
| 3.1158 | 1.3226 | 82 | 2.6645 | 0.1347 | 0.0423 | 0.1109 | 0.1108 | 20.0 |
| 2.9694 | 1.3548 | 84 | 2.6570 | 0.1344 | 0.0419 | 0.1107 | 0.1106 | 20.0 |
| 2.8569 | 1.3871 | 86 | 2.6498 | 0.135 | 0.0419 | 0.1108 | 0.1108 | 20.0 |
| 2.9821 | 1.4194 | 88 | 2.6431 | 0.1344 | 0.0412 | 0.1107 | 0.1107 | 20.0 |
| 2.9598 | 1.4516 | 90 | 2.6366 | 0.1351 | 0.0415 | 0.111 | 0.111 | 20.0 |
| 3.0488 | 1.4839 | 92 | 2.6303 | 0.1357 | 0.0423 | 0.1113 | 0.1112 | 20.0 |
| 2.7617 | 1.5161 | 94 | 2.6244 | 0.1364 | 0.0429 | 0.1114 | 0.1113 | 20.0 |
| 2.9448 | 1.5484 | 96 | 2.6187 | 0.1366 | 0.0431 | 0.1121 | 0.1119 | 20.0 |
| 2.6405 | 1.5806 | 98 | 2.6133 | 0.1373 | 0.0434 | 0.1126 | 0.1123 | 20.0 |
| 3.0242 | 1.6129 | 100 | 2.6079 | 0.1374 | 0.0429 | 0.1129 | 0.1126 | 20.0 |
| 2.7386 | 1.6452 | 102 | 2.6030 | 0.1375 | 0.0431 | 0.1131 | 0.1129 | 20.0 |
| 2.9335 | 1.6774 | 104 | 2.5981 | 0.1381 | 0.0435 | 0.1133 | 0.1131 | 20.0 |
| 2.8766 | 1.7097 | 106 | 2.5933 | 0.1381 | 0.0433 | 0.1135 | 0.1133 | 20.0 |
| 2.9737 | 1.7419 | 108 | 2.5885 | 0.138 | 0.0424 | 0.113 | 0.1129 | 20.0 |
| 2.8178 | 1.7742 | 110 | 2.5839 | 0.1389 | 0.0443 | 0.114 | 0.114 | 20.0 |
| 2.7075 | 1.8065 | 112 | 2.5798 | 0.1386 | 0.0442 | 0.1138 | 0.1137 | 20.0 |
| 2.9053 | 1.8387 | 114 | 2.5758 | 0.139 | 0.0444 | 0.114 | 0.1139 | 20.0 |
| 2.8273 | 1.8710 | 116 | 2.5722 | 0.1393 | 0.0444 | 0.1142 | 0.1141 | 20.0 |
| 2.7953 | 1.9032 | 118 | 2.5688 | 0.1389 | 0.0443 | 0.1138 | 0.1137 | 20.0 |
| 2.8765 | 1.9355 | 120 | 2.5653 | 0.1395 | 0.044 | 0.114 | 0.1139 | 20.0 |
| 3.032 | 1.9677 | 122 | 2.5620 | 0.1399 | 0.0439 | 0.1144 | 0.1142 | 20.0 |
| 2.924 | 2.0 | 124 | 2.5587 | 0.1398 | 0.0429 | 0.1138 | 0.1136 | 20.0 |
| 2.7613 | 2.0323 | 126 | 2.5553 | 0.1423 | 0.0441 | 0.1155 | 0.1154 | 20.0 |
| 2.5683 | 2.0645 | 128 | 2.5524 | 0.1422 | 0.0438 | 0.1153 | 0.1153 | 20.0 |
| 2.9889 | 2.0968 | 130 | 2.5496 | 0.1435 | 0.0447 | 0.1161 | 0.1161 | 20.0 |
| 2.716 | 2.1290 | 132 | 2.5470 | 0.1434 | 0.0452 | 0.116 | 0.1159 | 20.0 |
| 2.7641 | 2.1613 | 134 | 2.5446 | 0.1436 | 0.0454 | 0.1162 | 0.116 | 20.0 |
| 2.7018 | 2.1935 | 136 | 2.5423 | 0.1445 | 0.0463 | 0.1168 | 0.1167 | 20.0 |
| 2.9242 | 2.2258 | 138 | 2.5399 | 0.1452 | 0.0473 | 0.118 | 0.1179 | 20.0 |
| 2.8682 | 2.2581 | 140 | 2.5375 | 0.1454 | 0.0477 | 0.1179 | 0.1178 | 20.0 |
| 2.9252 | 2.2903 | 142 | 2.5350 | 0.1445 | 0.0473 | 0.117 | 0.1169 | 20.0 |
| 2.7431 | 2.3226 | 144 | 2.5326 | 0.1451 | 0.0482 | 0.1172 | 0.1171 | 20.0 |
| 2.8954 | 2.3548 | 146 | 2.5301 | 0.1454 | 0.048 | 0.1174 | 0.1173 | 20.0 |
| 2.8551 | 2.3871 | 148 | 2.5276 | 0.1458 | 0.0482 | 0.1174 | 0.1173 | 20.0 |
| 2.8506 | 2.4194 | 150 | 2.5253 | 0.1458 | 0.0482 | 0.1174 | 0.1173 | 20.0 |
| 2.809 | 2.4516 | 152 | 2.5232 | 0.1464 | 0.0485 | 0.1176 | 0.1176 | 20.0 |
| 2.9892 | 2.4839 | 154 | 2.5212 | 0.1465 | 0.0483 | 0.1174 | 0.1173 | 20.0 |
| 2.7943 | 2.5161 | 156 | 2.5194 | 0.1476 | 0.0496 | 0.1187 | 0.1186 | 20.0 |
| 2.9091 | 2.5484 | 158 | 2.5177 | 0.1474 | 0.0489 | 0.1183 | 0.1182 | 20.0 |
| 2.7993 | 2.5806 | 160 | 2.5159 | 0.148 | 0.0493 | 0.1192 | 0.119 | 20.0 |
| 2.5604 | 2.6129 | 162 | 2.5144 | 0.1484 | 0.0501 | 0.1197 | 0.1194 | 20.0 |
| 2.6414 | 2.6452 | 164 | 2.5128 | 0.1483 | 0.0499 | 0.1195 | 0.1193 | 20.0 |
| 2.8139 | 2.6774 | 166 | 2.5114 | 0.1481 | 0.0497 | 0.1194 | 0.1192 | 20.0 |
| 2.5727 | 2.7097 | 168 | 2.5100 | 0.1479 | 0.0496 | 0.1194 | 0.1192 | 20.0 |
| 2.8696 | 2.7419 | 170 | 2.5086 | 0.1473 | 0.0491 | 0.1189 | 0.1187 | 20.0 |
| 2.5752 | 2.7742 | 172 | 2.5072 | 0.1475 | 0.0494 | 0.1192 | 0.1189 | 20.0 |
| 2.6993 | 2.8065 | 174 | 2.5061 | 0.1475 | 0.0494 | 0.1192 | 0.1189 | 20.0 |
| 2.9975 | 2.8387 | 176 | 2.5050 | 0.1471 | 0.0491 | 0.1189 | 0.1186 | 20.0 |
| 2.742 | 2.8710 | 178 | 2.5040 | 0.148 | 0.0497 | 0.1196 | 0.1194 | 20.0 |
| 2.9057 | 2.9032 | 180 | 2.5029 | 0.1484 | 0.05 | 0.12 | 0.1198 | 20.0 |
| 2.6459 | 2.9355 | 182 | 2.5019 | 0.1485 | 0.05 | 0.1203 | 0.12 | 20.0 |
| 2.8267 | 2.9677 | 184 | 2.5008 | 0.1482 | 0.0496 | 0.12 | 0.1198 | 20.0 |
| 2.8905 | 3.0 | 186 | 2.4999 | 0.1486 | 0.0501 | 0.1202 | 0.12 | 20.0 |
| 2.8097 | 3.0323 | 188 | 2.4989 | 0.149 | 0.0508 | 0.1206 | 0.1204 | 20.0 |
| 2.9053 | 3.0645 | 190 | 2.4979 | 0.1498 | 0.0509 | 0.1214 | 0.1213 | 20.0 |
| 2.6652 | 3.0968 | 192 | 2.4970 | 0.1498 | 0.0509 | 0.1214 | 0.1213 | 20.0 |
| 2.6554 | 3.1290 | 194 | 2.4961 | 0.1501 | 0.0511 | 0.1215 | 0.1214 | 20.0 |
| 2.7374 | 3.1613 | 196 | 2.4952 | 0.1498 | 0.0509 | 0.1214 | 0.1213 | 20.0 |
| 2.9662 | 3.1935 | 198 | 2.4943 | 0.15 | 0.0511 | 0.1215 | 0.1213 | 20.0 |
| 2.6848 | 3.2258 | 200 | 2.4936 | 0.15 | 0.0514 | 0.1215 | 0.1213 | 20.0 |
| 2.8112 | 3.2581 | 202 | 2.4929 | 0.1501 | 0.0515 | 0.1217 | 0.1215 | 20.0 |
| 3.1429 | 3.2903 | 204 | 2.4923 | 0.1502 | 0.0515 | 0.1216 | 0.1214 | 20.0 |
| 2.5538 | 3.3226 | 206 | 2.4917 | 0.1502 | 0.0515 | 0.1216 | 0.1214 | 20.0 |
| 2.783 | 3.3548 | 208 | 2.4911 | 0.1513 | 0.0524 | 0.1225 | 0.1223 | 20.0 |
| 2.6299 | 3.3871 | 210 | 2.4905 | 0.1513 | 0.0521 | 0.1224 | 0.1222 | 20.0 |
| 2.8 | 3.4194 | 212 | 2.4900 | 0.1508 | 0.0523 | 0.1224 | 0.1223 | 20.0 |
| 2.6194 | 3.4516 | 214 | 2.4895 | 0.1509 | 0.0523 | 0.1225 | 0.1224 | 20.0 |
| 2.6382 | 3.4839 | 216 | 2.4890 | 0.1509 | 0.0523 | 0.1225 | 0.1224 | 20.0 |
| 2.6625 | 3.5161 | 218 | 2.4886 | 0.1508 | 0.0522 | 0.1226 | 0.1225 | 20.0 |
| 2.583 | 3.5484 | 220 | 2.4882 | 0.1507 | 0.0522 | 0.1226 | 0.1225 | 20.0 |
| 2.9198 | 3.5806 | 222 | 2.4878 | 0.1506 | 0.0522 | 0.1225 | 0.1224 | 20.0 |
| 2.9293 | 3.6129 | 224 | 2.4875 | 0.151 | 0.0522 | 0.1229 | 0.1228 | 20.0 |
| 2.9123 | 3.6452 | 226 | 2.4871 | 0.1511 | 0.0525 | 0.1233 | 0.1231 | 20.0 |
| 2.6949 | 3.6774 | 228 | 2.4867 | 0.1508 | 0.0522 | 0.1228 | 0.1227 | 20.0 |
| 2.7701 | 3.7097 | 230 | 2.4864 | 0.1509 | 0.0522 | 0.1229 | 0.1228 | 20.0 |
| 2.584 | 3.7419 | 232 | 2.4861 | 0.1511 | 0.0524 | 0.1231 | 0.123 | 20.0 |
| 2.7498 | 3.7742 | 234 | 2.4859 | 0.1512 | 0.0522 | 0.123 | 0.1229 | 20.0 |
| 2.85 | 3.8065 | 236 | 2.4856 | 0.1515 | 0.0527 | 0.1236 | 0.1235 | 20.0 |
| 2.7835 | 3.8387 | 238 | 2.4855 | 0.1512 | 0.0524 | 0.1232 | 0.1231 | 20.0 |
| 2.7089 | 3.8710 | 240 | 2.4853 | 0.1513 | 0.0525 | 0.1234 | 0.1232 | 20.0 |
| 2.7233 | 3.9032 | 242 | 2.4852 | 0.1514 | 0.0525 | 0.1234 | 0.1232 | 20.0 |
| 2.653 | 3.9355 | 244 | 2.4851 | 0.1518 | 0.0526 | 0.1237 | 0.1235 | 20.0 |
| 2.7108 | 3.9677 | 246 | 2.4850 | 0.1519 | 0.0528 | 0.1238 | 0.1237 | 20.0 |
| 2.7351 | 4.0 | 248 | 2.4850 | 0.1516 | 0.0525 | 0.1235 | 0.1233 | 20.0 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
jack584/blockassist-bc-sneaky_dextrous_deer_1754896040
|
jack584
| 2025-08-11T07:08:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sneaky dextrous deer",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:08:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sneaky dextrous deer
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
LarryAIDraw/ZZZ_Yixuan
|
LarryAIDraw
| 2025-08-11T07:07:44Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-08-11T07:06:55Z |
---
license: creativeml-openrail-m
---
|
ecamli/blockassist-bc-hulking_soft_hippo_1754895983
|
ecamli
| 2025-08-11T07:07:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hulking soft hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:06:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hulking soft hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
minhtien2405/vovinam-wav2vec2-base-vi
|
minhtien2405
| 2025-08-11T07:05:43Z | 178 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"speech-recognition",
"vietnamese",
"vietnam",
"voviai",
"vovinam",
"generated_from_trainer",
"vi",
"base_model:minhtien2405/wav2vec2-base-vi",
"base_model:finetune:minhtien2405/wav2vec2-base-vi",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-05T17:16:56Z |
---
library_name: transformers
language:
- vi
license: cc-by-nc-4.0
base_model: minhtien2405/wav2vec2-base-vi
tags:
- speech-recognition
- vietnamese
- vietnam
- voviai
- vovinam
- generated_from_trainer
metrics:
- wer
model-index:
- name: vovinam-wav2vec2-base-vi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vovinam-wav2vec2-base-vi
This model is a fine-tuned version of [minhtien2405/wav2vec2-base-vi](https://huggingface.co/minhtien2405/wav2vec2-base-vi) on the minhtien2405/VoviAIDataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0657
- Wer: 0.0967
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 0.7084 | 0.2413 | 100 | 0.4609 | 0.3103 |
| 0.6193 | 0.4825 | 200 | 0.4034 | 0.2812 |
| 0.5565 | 0.7238 | 300 | 0.3769 | 0.2592 |
| 0.5444 | 0.9650 | 400 | 0.3177 | 0.2376 |
| 0.4498 | 1.2051 | 500 | 0.2961 | 0.2211 |
| 0.4444 | 1.4463 | 600 | 0.2689 | 0.2153 |
| 0.4495 | 1.6876 | 700 | 0.2312 | 0.2023 |
| 0.3887 | 1.9288 | 800 | 0.2392 | 0.1943 |
| 0.3425 | 2.1689 | 900 | 0.2424 | 0.1930 |
| 0.3801 | 2.4101 | 1000 | 0.2223 | 0.1864 |
| 0.3344 | 2.6514 | 1100 | 0.2196 | 0.1822 |
| 0.3239 | 2.8926 | 1200 | 0.1846 | 0.1709 |
| 0.2972 | 3.1327 | 1300 | 0.1708 | 0.1597 |
| 0.2996 | 3.3739 | 1400 | 0.1875 | 0.1687 |
| 0.2752 | 3.6152 | 1500 | 0.1885 | 0.1629 |
| 0.2953 | 3.8565 | 1600 | 0.2027 | 0.1592 |
| 0.249 | 4.0965 | 1700 | 0.1725 | 0.1554 |
| 0.2596 | 4.3378 | 1800 | 0.1774 | 0.1593 |
| 0.2572 | 4.5790 | 1900 | 0.1583 | 0.1516 |
| 0.2642 | 4.8203 | 2000 | 0.1656 | 0.1555 |
| 0.2263 | 5.0603 | 2100 | 0.1425 | 0.1470 |
| 0.2293 | 5.3016 | 2200 | 0.1376 | 0.1401 |
| 0.2208 | 5.5428 | 2300 | 0.1448 | 0.1387 |
| 0.2187 | 5.7841 | 2400 | 0.1414 | 0.1381 |
| 0.2224 | 6.0241 | 2500 | 0.1587 | 0.1445 |
| 0.2137 | 6.2654 | 2600 | 0.1350 | 0.1436 |
| 0.198 | 6.5066 | 2700 | 0.1501 | 0.1397 |
| 0.1901 | 6.7479 | 2800 | 0.1407 | 0.1385 |
| 0.201 | 6.9891 | 2900 | 0.1542 | 0.1439 |
| 0.1916 | 7.2292 | 3000 | 0.1506 | 0.1450 |
| 0.1815 | 7.4704 | 3100 | 0.1372 | 0.1384 |
| 0.1735 | 7.7117 | 3200 | 0.1350 | 0.1317 |
| 0.1857 | 7.9530 | 3300 | 0.1489 | 0.1396 |
| 0.1627 | 8.1930 | 3400 | 0.1352 | 0.1321 |
| 0.1944 | 8.4343 | 3500 | 0.1173 | 0.1297 |
| 0.1834 | 8.6755 | 3600 | 0.1230 | 0.1286 |
| 0.1713 | 8.9168 | 3700 | 0.1248 | 0.1306 |
| 0.1523 | 9.1568 | 3800 | 0.1228 | 0.1348 |
| 0.1534 | 9.3981 | 3900 | 0.1139 | 0.1317 |
| 0.1583 | 9.6393 | 4000 | 0.0971 | 0.1195 |
| 0.1541 | 9.8806 | 4100 | 0.1144 | 0.1306 |
| 0.1348 | 10.1206 | 4200 | 0.1238 | 0.1315 |
| 0.1426 | 10.3619 | 4300 | 0.1248 | 0.1234 |
| 0.1538 | 10.6031 | 4400 | 0.1238 | 0.1264 |
| 0.1534 | 10.8444 | 4500 | 0.1341 | 0.1317 |
| 0.1516 | 11.0844 | 4600 | 0.1041 | 0.1239 |
| 0.1402 | 11.3257 | 4700 | 0.1132 | 0.1262 |
| 0.1438 | 11.5669 | 4800 | 0.1019 | 0.1172 |
| 0.1398 | 11.8082 | 4900 | 0.1047 | 0.1228 |
| 0.1363 | 12.0483 | 5000 | 0.1151 | 0.1196 |
| 0.1307 | 12.2895 | 5100 | 0.1157 | 0.1229 |
| 0.133 | 12.5308 | 5200 | 0.1147 | 0.1222 |
| 0.1343 | 12.7720 | 5300 | 0.1010 | 0.1190 |
| 0.134 | 13.0121 | 5400 | 0.1092 | 0.1227 |
| 0.128 | 13.2533 | 5500 | 0.1002 | 0.1204 |
| 0.1254 | 13.4946 | 5600 | 0.1164 | 0.1224 |
| 0.1243 | 13.7358 | 5700 | 0.0977 | 0.1158 |
| 0.1316 | 13.9771 | 5800 | 0.1024 | 0.1172 |
| 0.1256 | 14.2171 | 5900 | 0.0923 | 0.1148 |
| 0.1244 | 14.4584 | 6000 | 0.1141 | 0.1220 |
| 0.1248 | 14.6996 | 6100 | 0.0989 | 0.1204 |
| 0.1212 | 14.9409 | 6200 | 0.0888 | 0.1151 |
| 0.131 | 15.1809 | 6300 | 0.0956 | 0.1145 |
| 0.1143 | 15.4222 | 6400 | 0.0901 | 0.1120 |
| 0.1179 | 15.6634 | 6500 | 0.1007 | 0.1185 |
| 0.1172 | 15.9047 | 6600 | 0.1031 | 0.1161 |
| 0.1012 | 16.1448 | 6700 | 0.0913 | 0.1159 |
| 0.0919 | 16.3860 | 6800 | 0.1028 | 0.1172 |
| 0.1072 | 16.6273 | 6900 | 0.1010 | 0.1184 |
| 0.0926 | 16.8685 | 7000 | 0.0909 | 0.1133 |
| 0.0995 | 17.1086 | 7100 | 0.0952 | 0.1150 |
| 0.1032 | 17.3498 | 7200 | 0.0905 | 0.1113 |
| 0.0967 | 17.5911 | 7300 | 0.0964 | 0.1158 |
| 0.0985 | 17.8323 | 7400 | 0.0991 | 0.1144 |
| 0.097 | 18.0724 | 7500 | 0.0853 | 0.1105 |
| 0.0956 | 18.3136 | 7600 | 0.0968 | 0.1124 |
| 0.102 | 18.5549 | 7700 | 0.0963 | 0.1131 |
| 0.1025 | 18.7961 | 7800 | 0.0874 | 0.1111 |
| 0.0968 | 19.0362 | 7900 | 0.0830 | 0.1095 |
| 0.0821 | 19.2774 | 8000 | 0.0955 | 0.1126 |
| 0.0877 | 19.5187 | 8100 | 0.0929 | 0.1122 |
| 0.0867 | 19.7600 | 8200 | 0.0843 | 0.1132 |
| 0.0836 | 20.0 | 8300 | 0.0901 | 0.1112 |
| 0.0886 | 20.2413 | 8400 | 0.0968 | 0.1161 |
| 0.0855 | 20.4825 | 8500 | 0.1025 | 0.1117 |
| 0.0868 | 20.7238 | 8600 | 0.1219 | 0.1151 |
| 0.0929 | 20.9650 | 8700 | 0.0992 | 0.1168 |
| 0.0744 | 21.2051 | 8800 | 0.0977 | 0.1151 |
| 0.0856 | 21.4463 | 8900 | 0.0958 | 0.1139 |
| 0.0791 | 21.6876 | 9000 | 0.1004 | 0.1142 |
| 0.091 | 21.9288 | 9100 | 0.1011 | 0.1184 |
| 0.0752 | 22.1689 | 9200 | 0.0995 | 0.1176 |
| 0.0785 | 22.4101 | 9300 | 0.0993 | 0.1105 |
| 0.0848 | 22.6514 | 9400 | 0.0794 | 0.1168 |
| 0.0808 | 22.8926 | 9500 | 0.0859 | 0.1099 |
| 0.0778 | 23.1327 | 9600 | 0.0862 | 0.1074 |
| 0.0748 | 23.3739 | 9700 | 0.0924 | 0.1132 |
| 0.0741 | 23.6152 | 9800 | 0.0880 | 0.1127 |
| 0.0741 | 23.8565 | 9900 | 0.0933 | 0.1121 |
| 0.0765 | 24.0965 | 10000 | 0.0819 | 0.1055 |
| 0.0656 | 24.3378 | 10100 | 0.0869 | 0.1068 |
| 0.0766 | 24.5790 | 10200 | 0.0748 | 0.1031 |
| 0.0647 | 24.8203 | 10300 | 0.0831 | 0.1046 |
| 0.0648 | 25.0603 | 10400 | 0.0774 | 0.1077 |
| 0.07 | 25.3016 | 10500 | 0.0817 | 0.1054 |
| 0.0713 | 25.5428 | 10600 | 0.0823 | 0.1069 |
| 0.0705 | 25.7841 | 10700 | 0.0800 | 0.1044 |
| 0.0622 | 26.0241 | 10800 | 0.0837 | 0.1093 |
| 0.0711 | 26.2654 | 10900 | 0.0798 | 0.1031 |
| 0.0607 | 26.5066 | 11000 | 0.0844 | 0.1046 |
| 0.0574 | 26.7479 | 11100 | 0.0799 | 0.1037 |
| 0.0491 | 26.9891 | 11200 | 0.0846 | 0.1052 |
| 0.0642 | 27.2292 | 11300 | 0.0752 | 0.1045 |
| 0.0674 | 27.4704 | 11400 | 0.0815 | 0.1068 |
| 0.0601 | 27.7117 | 11500 | 0.0816 | 0.1067 |
| 0.0584 | 27.9530 | 11600 | 0.0711 | 0.1076 |
| 0.0705 | 28.1930 | 11700 | 0.0729 | 0.1058 |
| 0.0535 | 28.4343 | 11800 | 0.0724 | 0.1048 |
| 0.0703 | 28.6755 | 11900 | 0.0798 | 0.1087 |
| 0.0527 | 28.9168 | 12000 | 0.0783 | 0.1043 |
| 0.0548 | 29.1568 | 12100 | 0.0743 | 0.1040 |
| 0.0435 | 29.3981 | 12200 | 0.0750 | 0.1038 |
| 0.0571 | 29.6393 | 12300 | 0.0653 | 0.1043 |
| 0.057 | 29.8806 | 12400 | 0.0705 | 0.1019 |
| 0.0514 | 30.1206 | 12500 | 0.0691 | 0.1009 |
| 0.0486 | 30.3619 | 12600 | 0.0698 | 0.1015 |
| 0.0514 | 30.6031 | 12700 | 0.0742 | 0.1019 |
| 0.0562 | 30.8444 | 12800 | 0.0772 | 0.1024 |
| 0.0662 | 31.0844 | 12900 | 0.0701 | 0.1019 |
| 0.0521 | 31.3257 | 13000 | 0.0696 | 0.1005 |
| 0.0438 | 31.5669 | 13100 | 0.0642 | 0.1000 |
| 0.0515 | 31.8082 | 13200 | 0.0677 | 0.1013 |
| 0.048 | 32.0483 | 13300 | 0.0615 | 0.1014 |
| 0.0485 | 32.2895 | 13400 | 0.0689 | 0.1017 |
| 0.0425 | 32.5308 | 13500 | 0.0750 | 0.1022 |
| 0.0482 | 32.7720 | 13600 | 0.0727 | 0.1043 |
| 0.0489 | 33.0121 | 13700 | 0.0604 | 0.0983 |
| 0.0408 | 33.2533 | 13800 | 0.0716 | 0.0990 |
| 0.0441 | 33.4946 | 13900 | 0.0741 | 0.1029 |
| 0.0401 | 33.7358 | 14000 | 0.0758 | 0.1008 |
| 0.0368 | 33.9771 | 14100 | 0.0779 | 0.0999 |
| 0.0498 | 34.2171 | 14200 | 0.0771 | 0.1026 |
| 0.0435 | 34.4584 | 14300 | 0.0693 | 0.1012 |
| 0.047 | 34.6996 | 14400 | 0.0663 | 0.1001 |
| 0.0479 | 34.9409 | 14500 | 0.0636 | 0.1000 |
| 0.0455 | 35.1809 | 14600 | 0.0658 | 0.1019 |
| 0.045 | 35.4222 | 14700 | 0.0718 | 0.0993 |
| 0.042 | 35.6634 | 14800 | 0.0785 | 0.1013 |
| 0.0451 | 35.9047 | 14900 | 0.0747 | 0.1017 |
| 0.0406 | 36.1448 | 15000 | 0.0719 | 0.1018 |
| 0.0403 | 36.3860 | 15100 | 0.0719 | 0.1052 |
| 0.036 | 36.6273 | 15200 | 0.0726 | 0.1018 |
| 0.0433 | 36.8685 | 15300 | 0.0781 | 0.1024 |
| 0.0373 | 37.1086 | 15400 | 0.0831 | 0.1020 |
| 0.0446 | 37.3498 | 15500 | 0.0878 | 0.1102 |
| 0.0452 | 37.5911 | 15600 | 0.0760 | 0.0997 |
| 0.0338 | 37.8323 | 15700 | 0.0733 | 0.0999 |
| 0.0388 | 38.0724 | 15800 | 0.0695 | 0.0989 |
| 0.0331 | 38.3136 | 15900 | 0.0732 | 0.0991 |
| 0.0328 | 38.5549 | 16000 | 0.0741 | 0.1020 |
| 0.0382 | 38.7961 | 16100 | 0.0685 | 0.1015 |
| 0.0387 | 39.0362 | 16200 | 0.0721 | 0.0998 |
| 0.0391 | 39.2774 | 16300 | 0.0689 | 0.0988 |
| 0.0357 | 39.5187 | 16400 | 0.0702 | 0.1011 |
| 0.0386 | 39.7600 | 16500 | 0.0673 | 0.1025 |
| 0.0333 | 40.0 | 16600 | 0.0662 | 0.1047 |
| 0.0255 | 40.2413 | 16700 | 0.0731 | 0.1073 |
| 0.0301 | 40.4825 | 16800 | 0.0669 | 0.0997 |
| 0.0296 | 40.7238 | 16900 | 0.0632 | 0.0982 |
| 0.0377 | 40.9650 | 17000 | 0.0649 | 0.0997 |
| 0.0448 | 41.2051 | 17100 | 0.0648 | 0.0993 |
| 0.0327 | 41.4463 | 17200 | 0.0699 | 0.0980 |
| 0.0267 | 41.6876 | 17300 | 0.0682 | 0.0990 |
| 0.0351 | 41.9288 | 17400 | 0.0630 | 0.0977 |
| 0.0379 | 42.1689 | 17500 | 0.0581 | 0.0963 |
| 0.0256 | 42.4101 | 17600 | 0.0604 | 0.0970 |
| 0.0289 | 42.6514 | 17700 | 0.0596 | 0.0963 |
| 0.0307 | 42.8926 | 17800 | 0.0604 | 0.0969 |
| 0.0241 | 43.1327 | 17900 | 0.0584 | 0.0981 |
| 0.0326 | 43.3739 | 18000 | 0.0581 | 0.0965 |
| 0.0282 | 43.6152 | 18100 | 0.0583 | 0.0967 |
| 0.0285 | 43.8565 | 18200 | 0.0579 | 0.0959 |
| 0.022 | 44.0965 | 18300 | 0.0654 | 0.0973 |
| 0.026 | 44.3378 | 18400 | 0.0640 | 0.0964 |
| 0.028 | 44.5790 | 18500 | 0.0627 | 0.0961 |
| 0.0288 | 44.8203 | 18600 | 0.0634 | 0.0962 |
| 0.025 | 45.0603 | 18700 | 0.0608 | 0.0961 |
| 0.0416 | 45.3016 | 18800 | 0.0610 | 0.0979 |
| 0.0311 | 45.5428 | 18900 | 0.0608 | 0.0968 |
| 0.0268 | 45.7841 | 19000 | 0.0575 | 0.0965 |
| 0.0249 | 46.0241 | 19100 | 0.0611 | 0.0960 |
| 0.0225 | 46.2654 | 19200 | 0.0594 | 0.0952 |
| 0.023 | 46.5066 | 19300 | 0.0595 | 0.0952 |
| 0.0291 | 46.7479 | 19400 | 0.0599 | 0.0955 |
| 0.0209 | 46.9891 | 19500 | 0.0620 | 0.0967 |
| 0.0234 | 47.2292 | 19600 | 0.0610 | 0.0956 |
| 0.0255 | 47.4704 | 19700 | 0.0611 | 0.0954 |
| 0.0289 | 47.7117 | 19800 | 0.0599 | 0.0956 |
| 0.0242 | 47.9530 | 19900 | 0.0619 | 0.0956 |
| 0.0195 | 48.1930 | 20000 | 0.0595 | 0.0951 |
| 0.0309 | 48.4343 | 20100 | 0.0600 | 0.0949 |
| 0.0233 | 48.6755 | 20200 | 0.0594 | 0.0952 |
| 0.0207 | 48.9168 | 20300 | 0.0589 | 0.0951 |
| 0.0212 | 49.1568 | 20400 | 0.0594 | 0.0952 |
| 0.0236 | 49.3981 | 20500 | 0.0598 | 0.0953 |
| 0.0271 | 49.6393 | 20600 | 0.0598 | 0.0953 |
| 0.0217 | 49.8806 | 20700 | 0.0599 | 0.0953 |
### Framework versions
- Transformers 4.53.0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2
|
ecamli/blockassist-bc-hulking_soft_hippo_1754895791
|
ecamli
| 2025-08-11T07:03:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hulking soft hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:03:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hulking soft hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kumoooo/blockassist-bc-aquatic_restless_camel_1754895111
|
kumoooo
| 2025-08-11T07:00:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic restless camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T07:00:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic restless camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1754895465
|
roeker
| 2025-08-11T06:58:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:58:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754895398
|
IvanJAjebu
| 2025-08-11T06:57:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:57:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pepppper/my_awesome_billsum_model
|
pepppper
| 2025-08-11T06:57:41Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T06:57:10Z |
---
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8019
- Rouge1: 0.1566
- Rouge2: 0.0608
- Rougel: 0.1284
- Rougelsum: 0.1284
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 4.9772 | 0.0323 | 2 | 4.9098 | 0.1456 | 0.0505 | 0.1212 | 0.1207 | 20.0 |
| 4.9172 | 0.0645 | 4 | 4.7930 | 0.145 | 0.0492 | 0.1204 | 0.1202 | 20.0 |
| 4.7805 | 0.0968 | 6 | 4.6848 | 0.1443 | 0.0492 | 0.1203 | 0.12 | 20.0 |
| 4.4786 | 0.1290 | 8 | 4.5023 | 0.1414 | 0.047 | 0.1178 | 0.1176 | 20.0 |
| 4.6631 | 0.1613 | 10 | 4.3689 | 0.1398 | 0.0451 | 0.1166 | 0.1161 | 20.0 |
| 4.3956 | 0.1935 | 12 | 4.2466 | 0.1386 | 0.0451 | 0.1165 | 0.1161 | 20.0 |
| 4.1188 | 0.2258 | 14 | 4.0433 | 0.1375 | 0.0441 | 0.1162 | 0.1159 | 20.0 |
| 4.1376 | 0.2581 | 16 | 3.9204 | 0.1368 | 0.0438 | 0.1166 | 0.1162 | 20.0 |
| 4.0215 | 0.2903 | 18 | 3.8223 | 0.1362 | 0.0432 | 0.1159 | 0.1156 | 20.0 |
| 4.0434 | 0.3226 | 20 | 3.7228 | 0.1371 | 0.0444 | 0.1167 | 0.1164 | 20.0 |
| 3.7677 | 0.3548 | 22 | 3.6486 | 0.1381 | 0.0454 | 0.1175 | 0.1173 | 20.0 |
| 3.8049 | 0.3871 | 24 | 3.5876 | 0.1369 | 0.0435 | 0.1163 | 0.1159 | 20.0 |
| 3.6867 | 0.4194 | 26 | 3.5269 | 0.1366 | 0.043 | 0.1162 | 0.1157 | 20.0 |
| 3.62 | 0.4516 | 28 | 3.4773 | 0.1325 | 0.0396 | 0.1125 | 0.1124 | 20.0 |
| 3.6324 | 0.4839 | 30 | 3.4397 | 0.1308 | 0.0383 | 0.111 | 0.1108 | 20.0 |
| 3.6464 | 0.5161 | 32 | 3.4071 | 0.1303 | 0.038 | 0.1107 | 0.1105 | 20.0 |
| 3.6456 | 0.5484 | 34 | 3.3747 | 0.1302 | 0.0379 | 0.1104 | 0.1102 | 20.0 |
| 3.4388 | 0.5806 | 36 | 3.3473 | 0.1291 | 0.0377 | 0.1092 | 0.109 | 20.0 |
| 3.6904 | 0.6129 | 38 | 3.3216 | 0.1299 | 0.038 | 0.11 | 0.1097 | 20.0 |
| 3.3703 | 0.6452 | 40 | 3.2946 | 0.1281 | 0.0361 | 0.1081 | 0.1079 | 20.0 |
| 3.4036 | 0.6774 | 42 | 3.2701 | 0.1277 | 0.0357 | 0.1074 | 0.1072 | 20.0 |
| 3.3292 | 0.7097 | 44 | 3.2474 | 0.1278 | 0.0356 | 0.1071 | 0.1067 | 20.0 |
| 3.4741 | 0.7419 | 46 | 3.2252 | 0.1283 | 0.035 | 0.1067 | 0.1065 | 20.0 |
| 3.2058 | 0.7742 | 48 | 3.2040 | 0.1295 | 0.0357 | 0.1071 | 0.1068 | 20.0 |
| 3.3241 | 0.8065 | 50 | 3.1842 | 0.1301 | 0.0366 | 0.1082 | 0.1079 | 20.0 |
| 3.2942 | 0.8387 | 52 | 3.1657 | 0.1303 | 0.0367 | 0.1086 | 0.1085 | 20.0 |
| 3.4451 | 0.8710 | 54 | 3.1482 | 0.1308 | 0.0365 | 0.109 | 0.1089 | 20.0 |
| 3.3112 | 0.9032 | 56 | 3.1319 | 0.1313 | 0.0365 | 0.1092 | 0.1091 | 20.0 |
| 3.4478 | 0.9355 | 58 | 3.1161 | 0.132 | 0.0369 | 0.1096 | 0.1095 | 20.0 |
| 3.2917 | 0.9677 | 60 | 3.1012 | 0.1304 | 0.0357 | 0.1081 | 0.1082 | 20.0 |
| 3.3915 | 1.0 | 62 | 3.0872 | 0.1308 | 0.0368 | 0.1088 | 0.1088 | 20.0 |
| 3.0503 | 1.0323 | 64 | 3.0738 | 0.1317 | 0.0377 | 0.1095 | 0.1096 | 20.0 |
| 3.2547 | 1.0645 | 66 | 3.0611 | 0.1325 | 0.0388 | 0.1105 | 0.1105 | 20.0 |
| 3.1897 | 1.0968 | 68 | 3.0493 | 0.1327 | 0.039 | 0.1108 | 0.1108 | 20.0 |
| 3.1737 | 1.1290 | 70 | 3.0381 | 0.1335 | 0.0393 | 0.1113 | 0.1112 | 20.0 |
| 3.1706 | 1.1613 | 72 | 3.0276 | 0.1331 | 0.0399 | 0.111 | 0.111 | 20.0 |
| 3.1955 | 1.1935 | 74 | 3.0177 | 0.1333 | 0.0403 | 0.111 | 0.111 | 20.0 |
| 2.9754 | 1.2258 | 76 | 3.0084 | 0.1341 | 0.0421 | 0.1118 | 0.1118 | 20.0 |
| 3.1798 | 1.2581 | 78 | 2.9997 | 0.1352 | 0.0428 | 0.1126 | 0.1126 | 20.0 |
| 3.2132 | 1.2903 | 80 | 2.9913 | 0.1364 | 0.0439 | 0.1132 | 0.1131 | 20.0 |
| 3.2655 | 1.3226 | 82 | 2.9835 | 0.1367 | 0.0443 | 0.1137 | 0.1136 | 20.0 |
| 3.2802 | 1.3548 | 84 | 2.9760 | 0.137 | 0.0442 | 0.1139 | 0.1138 | 20.0 |
| 2.9521 | 1.3871 | 86 | 2.9689 | 0.1374 | 0.0435 | 0.1135 | 0.1133 | 20.0 |
| 3.0009 | 1.4194 | 88 | 2.9622 | 0.1382 | 0.0446 | 0.1137 | 0.1137 | 20.0 |
| 2.8817 | 1.4516 | 90 | 2.9560 | 0.1388 | 0.0452 | 0.1139 | 0.1139 | 20.0 |
| 3.0443 | 1.4839 | 92 | 2.9499 | 0.1394 | 0.0462 | 0.1142 | 0.1142 | 20.0 |
| 3.1485 | 1.5161 | 94 | 2.9439 | 0.1417 | 0.0482 | 0.1158 | 0.1161 | 20.0 |
| 3.1887 | 1.5484 | 96 | 2.9383 | 0.1424 | 0.0489 | 0.1163 | 0.1165 | 20.0 |
| 3.1322 | 1.5806 | 98 | 2.9328 | 0.1429 | 0.0496 | 0.1167 | 0.1169 | 20.0 |
| 3.3135 | 1.6129 | 100 | 2.9274 | 0.1438 | 0.0501 | 0.1171 | 0.1174 | 20.0 |
| 2.8948 | 1.6452 | 102 | 2.9222 | 0.1445 | 0.0509 | 0.1178 | 0.1181 | 20.0 |
| 3.1387 | 1.6774 | 104 | 2.9174 | 0.1443 | 0.0504 | 0.1173 | 0.1175 | 20.0 |
| 3.2514 | 1.7097 | 106 | 2.9125 | 0.1462 | 0.0533 | 0.1193 | 0.1194 | 20.0 |
| 2.7514 | 1.7419 | 108 | 2.9080 | 0.1463 | 0.0536 | 0.1194 | 0.1194 | 20.0 |
| 3.0971 | 1.7742 | 110 | 2.9036 | 0.1461 | 0.0533 | 0.1193 | 0.1194 | 20.0 |
| 3.084 | 1.8065 | 112 | 2.8995 | 0.146 | 0.0533 | 0.1191 | 0.1193 | 20.0 |
| 3.0102 | 1.8387 | 114 | 2.8954 | 0.1466 | 0.0541 | 0.1195 | 0.1195 | 20.0 |
| 3.1742 | 1.8710 | 116 | 2.8915 | 0.1467 | 0.0549 | 0.1201 | 0.12 | 20.0 |
| 3.1178 | 1.9032 | 118 | 2.8878 | 0.1473 | 0.0555 | 0.1202 | 0.1202 | 20.0 |
| 3.1223 | 1.9355 | 120 | 2.8841 | 0.1477 | 0.0559 | 0.1203 | 0.1204 | 20.0 |
| 3.1209 | 1.9677 | 122 | 2.8805 | 0.147 | 0.0557 | 0.1196 | 0.1196 | 20.0 |
| 3.0821 | 2.0 | 124 | 2.8772 | 0.1471 | 0.0555 | 0.1194 | 0.1194 | 20.0 |
| 3.0732 | 2.0323 | 126 | 2.8741 | 0.1481 | 0.0557 | 0.1202 | 0.1203 | 20.0 |
| 2.9747 | 2.0645 | 128 | 2.8709 | 0.1486 | 0.0563 | 0.1208 | 0.1208 | 20.0 |
| 2.9165 | 2.0968 | 130 | 2.8680 | 0.1494 | 0.057 | 0.1216 | 0.1217 | 20.0 |
| 3.2219 | 2.1290 | 132 | 2.8652 | 0.1507 | 0.0576 | 0.1227 | 0.123 | 20.0 |
| 2.9149 | 2.1613 | 134 | 2.8624 | 0.1516 | 0.0584 | 0.1237 | 0.1238 | 20.0 |
| 2.946 | 2.1935 | 136 | 2.8598 | 0.152 | 0.0582 | 0.124 | 0.124 | 20.0 |
| 2.9566 | 2.2258 | 138 | 2.8573 | 0.1541 | 0.06 | 0.1254 | 0.1254 | 20.0 |
| 3.1244 | 2.2581 | 140 | 2.8548 | 0.1546 | 0.0604 | 0.1262 | 0.1262 | 20.0 |
| 3.1096 | 2.2903 | 142 | 2.8524 | 0.1549 | 0.0598 | 0.126 | 0.1261 | 20.0 |
| 3.1272 | 2.3226 | 144 | 2.8501 | 0.155 | 0.0597 | 0.1261 | 0.1263 | 20.0 |
| 2.9613 | 2.3548 | 146 | 2.8478 | 0.1562 | 0.0607 | 0.127 | 0.1271 | 20.0 |
| 3.0311 | 2.3871 | 148 | 2.8455 | 0.1564 | 0.0607 | 0.127 | 0.1271 | 20.0 |
| 2.9894 | 2.4194 | 150 | 2.8435 | 0.156 | 0.0607 | 0.1269 | 0.1269 | 20.0 |
| 2.9377 | 2.4516 | 152 | 2.8414 | 0.1554 | 0.0612 | 0.1271 | 0.1272 | 20.0 |
| 3.2074 | 2.4839 | 154 | 2.8393 | 0.1554 | 0.061 | 0.1274 | 0.1273 | 20.0 |
| 2.7732 | 2.5161 | 156 | 2.8374 | 0.1561 | 0.0614 | 0.1279 | 0.128 | 20.0 |
| 3.1669 | 2.5484 | 158 | 2.8355 | 0.1559 | 0.0617 | 0.128 | 0.128 | 20.0 |
| 2.8896 | 2.5806 | 160 | 2.8337 | 0.1568 | 0.0621 | 0.1283 | 0.1284 | 20.0 |
| 3.3097 | 2.6129 | 162 | 2.8321 | 0.1566 | 0.0613 | 0.1277 | 0.1278 | 20.0 |
| 2.9491 | 2.6452 | 164 | 2.8307 | 0.1555 | 0.0603 | 0.1267 | 0.1268 | 20.0 |
| 3.1262 | 2.6774 | 166 | 2.8292 | 0.1554 | 0.0598 | 0.1265 | 0.1265 | 20.0 |
| 3.0347 | 2.7097 | 168 | 2.8277 | 0.1553 | 0.0597 | 0.1262 | 0.1263 | 20.0 |
| 2.9986 | 2.7419 | 170 | 2.8263 | 0.1552 | 0.0595 | 0.1261 | 0.1261 | 20.0 |
| 2.9333 | 2.7742 | 172 | 2.8249 | 0.1552 | 0.0595 | 0.1263 | 0.1263 | 20.0 |
| 2.8779 | 2.8065 | 174 | 2.8237 | 0.1545 | 0.059 | 0.1259 | 0.1258 | 20.0 |
| 2.7269 | 2.8387 | 176 | 2.8225 | 0.1544 | 0.059 | 0.1258 | 0.1257 | 20.0 |
| 2.8611 | 2.8710 | 178 | 2.8214 | 0.1563 | 0.0607 | 0.1271 | 0.1269 | 20.0 |
| 2.9573 | 2.9032 | 180 | 2.8204 | 0.1563 | 0.0607 | 0.1271 | 0.1269 | 20.0 |
| 2.7588 | 2.9355 | 182 | 2.8194 | 0.157 | 0.0617 | 0.128 | 0.1279 | 20.0 |
| 2.9015 | 2.9677 | 184 | 2.8184 | 0.1564 | 0.0613 | 0.1274 | 0.1275 | 20.0 |
| 2.7194 | 3.0 | 186 | 2.8175 | 0.1566 | 0.0615 | 0.1275 | 0.1276 | 20.0 |
| 3.1051 | 3.0323 | 188 | 2.8165 | 0.1565 | 0.0611 | 0.1271 | 0.1272 | 20.0 |
| 2.9184 | 3.0645 | 190 | 2.8156 | 0.1569 | 0.0615 | 0.1276 | 0.1277 | 20.0 |
| 3.0363 | 3.0968 | 192 | 2.8146 | 0.1577 | 0.0622 | 0.1283 | 0.1283 | 20.0 |
| 2.9219 | 3.1290 | 194 | 2.8137 | 0.1581 | 0.0624 | 0.1285 | 0.1286 | 20.0 |
| 3.051 | 3.1613 | 196 | 2.8128 | 0.1584 | 0.0627 | 0.1288 | 0.1288 | 20.0 |
| 3.2344 | 3.1935 | 198 | 2.8118 | 0.1585 | 0.0629 | 0.1289 | 0.1289 | 20.0 |
| 3.0232 | 3.2258 | 200 | 2.8110 | 0.1583 | 0.0621 | 0.1285 | 0.1286 | 20.0 |
| 3.1109 | 3.2581 | 202 | 2.8102 | 0.1594 | 0.0631 | 0.1292 | 0.1292 | 20.0 |
| 3.0242 | 3.2903 | 204 | 2.8094 | 0.1589 | 0.0627 | 0.1291 | 0.1293 | 20.0 |
| 2.9549 | 3.3226 | 206 | 2.8087 | 0.1588 | 0.0628 | 0.1292 | 0.1293 | 20.0 |
| 2.8961 | 3.3548 | 208 | 2.8080 | 0.1592 | 0.0632 | 0.1294 | 0.1294 | 20.0 |
| 2.8591 | 3.3871 | 210 | 2.8074 | 0.1577 | 0.0618 | 0.1283 | 0.1283 | 20.0 |
| 2.8098 | 3.4194 | 212 | 2.8069 | 0.1572 | 0.0614 | 0.1282 | 0.1282 | 20.0 |
| 2.9019 | 3.4516 | 214 | 2.8063 | 0.1572 | 0.0614 | 0.1282 | 0.1282 | 20.0 |
| 2.9847 | 3.4839 | 216 | 2.8058 | 0.1574 | 0.0615 | 0.1284 | 0.1285 | 20.0 |
| 2.9803 | 3.5161 | 218 | 2.8053 | 0.1572 | 0.0615 | 0.1283 | 0.1284 | 20.0 |
| 2.7936 | 3.5484 | 220 | 2.8048 | 0.1571 | 0.0615 | 0.1282 | 0.1283 | 20.0 |
| 2.8702 | 3.5806 | 222 | 2.8045 | 0.1572 | 0.0616 | 0.1283 | 0.1284 | 20.0 |
| 3.0268 | 3.6129 | 224 | 2.8041 | 0.157 | 0.0614 | 0.1282 | 0.1283 | 20.0 |
| 2.8437 | 3.6452 | 226 | 2.8037 | 0.1573 | 0.0615 | 0.1284 | 0.1284 | 20.0 |
| 3.026 | 3.6774 | 228 | 2.8034 | 0.1573 | 0.0615 | 0.1284 | 0.1284 | 20.0 |
| 2.8364 | 3.7097 | 230 | 2.8032 | 0.1569 | 0.061 | 0.1282 | 0.1282 | 20.0 |
| 3.0897 | 3.7419 | 232 | 2.8029 | 0.1569 | 0.061 | 0.1282 | 0.1282 | 20.0 |
| 2.9625 | 3.7742 | 234 | 2.8027 | 0.157 | 0.061 | 0.1283 | 0.1283 | 20.0 |
| 2.9021 | 3.8065 | 236 | 2.8025 | 0.1565 | 0.061 | 0.1282 | 0.1281 | 20.0 |
| 2.7147 | 3.8387 | 238 | 2.8023 | 0.1568 | 0.0609 | 0.1286 | 0.1286 | 20.0 |
| 2.995 | 3.8710 | 240 | 2.8022 | 0.1564 | 0.0608 | 0.1283 | 0.1283 | 20.0 |
| 2.9107 | 3.9032 | 242 | 2.8021 | 0.1564 | 0.0608 | 0.1283 | 0.1283 | 20.0 |
| 2.8883 | 3.9355 | 244 | 2.8020 | 0.1568 | 0.0609 | 0.1286 | 0.1286 | 20.0 |
| 2.928 | 3.9677 | 246 | 2.8020 | 0.1568 | 0.0609 | 0.1286 | 0.1286 | 20.0 |
| 2.627 | 4.0 | 248 | 2.8019 | 0.1566 | 0.0608 | 0.1284 | 0.1284 | 20.0 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
bapi2025/blockassist-bc-lanky_silky_duck_1754893785
|
bapi2025
| 2025-08-11T06:57:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lanky silky duck",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:53:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lanky silky duck
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ravifission/lora_Qwen3_0.6B_model_q4_k_m_gguf_aug11.gguf
|
ravifission
| 2025-08-11T06:56:29Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-11T06:55:43Z |
---
base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ravifission
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-0.6b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1754895321
|
lqpl
| 2025-08-11T06:56:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:56:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
JagjeevanAK/Speech-emotion-detection
|
JagjeevanAK
| 2025-08-11T06:56:06Z | 0 | 0 |
tensorflow
|
[
"tensorflow",
"audio",
"speech",
"emotion-recognition",
"keras",
"audio-classification",
"ravdess",
"en",
"dataset:ravdess",
"license:mit",
"model-index",
"region:us"
] |
audio-classification
| 2025-08-11T06:39:52Z |
---
language:
- en
license: mit
tags:
- audio
- speech
- emotion-recognition
- tensorflow
- keras
- audio-classification
- ravdess
datasets:
- ravdess
metrics:
- accuracy
- f1
model-index:
- name: Speech Emotion Recognition
results:
- task:
type: audio-classification
name: Audio Classification
dataset:
type: ravdess
name: RAVDESS
metrics:
- type: accuracy
name: Accuracy
value: "See confusion matrix"
pipeline_tag: audio-classification
library_name: tensorflow
---
# Speech Emotion Recognition Model
This model performs speech emotion recognition, classifying audio into 8 different emotional states.
## Model Description
This is a deep learning model trained to recognize emotions from speech audio. The model can classify audio into the following emotions:
- 😐 Neutral
- 😌 Calm
- 😊 Happy
- 😢 Sad
- 😠 Angry
- 😨 Fearful
- 🤢 Disgust
- 😲 Surprised
## Model Architecture
The model uses audio features extraction including:
- MFCC (Mel-frequency cepstral coefficients)
- Chroma features
- Mel-spectrogram features
## Usage
```python
import librosa
import numpy as np
from tensorflow.keras.models import load_model
# Load the model
model = load_model('trained_model.h5')
# Load and preprocess audio
def extract_feature(data, sr, mfcc=True, chroma=True, mel=True):
result = np.array([])
if mfcc:
mfccs = np.mean(librosa.feature.mfcc(y=data, sr=sr, n_mfcc=40).T, axis=0)
result = np.hstack((result, mfccs))
if chroma:
stft = np.abs(librosa.stft(data))
chroma_feat = np.mean(librosa.feature.chroma_stft(S=stft, sr=sr).T, axis=0)
result = np.hstack((result, chroma_feat))
if mel:
mel_feat = np.mean(librosa.feature.melspectrogram(y=data, sr=sr).T, axis=0)
result = np.hstack((result, mel_feat))
return result
# Load audio file
audio_path = "your_audio_file.wav"
data, sr = librosa.load(audio_path, sr=22050)
# Extract features
feature = extract_feature(data, sr, mfcc=True, chroma=True, mel=True)
feature = np.expand_dims(feature, axis=0)
feature = np.expand_dims(feature, axis=2)
# Make prediction
prediction = model.predict(feature)
predicted_class = np.argmax(prediction, axis=1)
# Map to emotion labels
emotions = {
0: 'Neutral',
1: 'Calm',
2: 'Happy',
3: 'Sad',
4: 'Angry',
5: 'Fearful',
6: 'Disgust',
7: 'Surprised'
}
predicted_emotion = emotions[predicted_class[0]]
print(f"Predicted emotion: {predicted_emotion}")
```
## Requirements
```
librosa
tensorflow
numpy
scikit-learn
```
## Training Data
The model was trained on the RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) dataset, which contains speech emotion recordings with the following emotion categories:
- Neutral
- Calm
- Happy
- Sad
- Angry
- Fearful
- Disgust
- Surprised
The dataset provides high-quality audio recordings from multiple speakers, allowing the model to learn robust emotion recognition patterns across different voices and speaking styles.
## Model Performance
The model has been trained and evaluated with the following performance metrics:
### Training Progress
The training curves show the model's learning progress over epochs, demonstrating convergence and good generalization.
### Confusion Matrix
The confusion matrix shows the model's performance on the RAVDESS dataset, demonstrating how well the model distinguishes between different emotional states.
## License
[Specify your license here]
## Citation
If you use this model, please cite:
```
@misc{speech-emotion-recognition,
author = {JagjeevanAK},
title = {Speech Emotion Recognition Model},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/JagjeevanAK/Speech-emotion-detection}
}
```
|
marcomaccarini/padella_nuova_1
|
marcomaccarini
| 2025-08-11T06:55:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T06:52:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
harisshahjellani122212/my-own-model
|
harisshahjellani122212
| 2025-08-11T06:53:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T06:53:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Out-of-Scope Use
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed]
|
roeker/blockassist-bc-quick_wiry_owl_1754895092
|
roeker
| 2025-08-11T06:53:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:52:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AsgharHussain/mera_pehla-model
|
AsgharHussain
| 2025-08-11T06:53:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T06:52:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ecamli/blockassist-bc-hulking_soft_hippo_1754895137
|
ecamli
| 2025-08-11T06:52:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hulking soft hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:52:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hulking soft hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754895092
|
ggozzy
| 2025-08-11T06:52:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:52:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754895046
|
IvanJAjebu
| 2025-08-11T06:52:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:51:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cyyin/ftllm_model
|
cyyin
| 2025-08-11T06:51:34Z | 19 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-08T02:19:00Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** cyyin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ravifission/lora_Qwen3_0.6B_model_unquantized_aug11
|
ravifission
| 2025-08-11T06:51:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T06:50:51Z |
---
base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ravifission
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-0.6b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
baichuan-inc/Baichuan-M2-32B-GPTQ-Int4
|
baichuan-inc
| 2025-08-11T06:51:16Z | 0 | 3 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"zh",
"base_model:Qwen/Qwen2.5-32B",
"base_model:quantized:Qwen/Qwen2.5-32B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2025-08-10T09:51:53Z |
---
license: apache-2.0
tags:
- chat
library_name: transformers
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-32B
---
# Baichuan-M2-32B-GPTQ-Int4
[](https://opensource.org/licenses/Apache-2.0)
[](https://huggingface.co/baichuan-inc/Baichuan-M2-32B)
[](https://huggingface.co/baichuan-inc/Baichuan-M2-32B-GPTQ-Int4)
[](https://modelers.cn/models/Baichuan/Baichuan-M2-32B-W8A8)
## 🌟 Model Overview
Baichuan-M2-32B is Baichuan AI's medical-enhanced reasoning model, the second medical model released by Baichuan. Designed for real-world medical reasoning tasks, this model builds upon Qwen2.5-32B with an innovative Large Verifier System. Through domain-specific fine-tuning on real-world medical questions, it achieves breakthrough medical performance while maintaining strong general capabilities.
**Model Features:**
Baichuan-M2 incorporates three core technical innovations: First, through the **Large Verifier System**, it combines medical scenario characteristics to design a comprehensive medical verification framework, including patient simulators and multi-dimensional verification mechanisms; second, through **medical domain adaptation enhancement** via Mid-Training, it achieves lightweight and efficient medical domain adaptation while preserving general capabilities; finally, it employs a **multi-stage reinforcement learning** strategy, decomposing complex RL tasks into hierarchical training stages to progressively enhance the model's medical knowledge, reasoning, and patient interaction capabilities.
**Core Highlights:**
- 🏆 **World's Leading Open-Source Medical Model**: Outperforms all open-source models and many proprietary models on HealthBench, achieving medical capabilities closest to GPT-5
- 🧠 **Doctor-Thinking Alignment**: Trained on real clinical cases and patient simulators, with clinical diagnostic thinking and robust patient interaction capabilities
- ⚡ **Efficient Deployment**: Supports 4-bit quantization for single-RTX4090 deployment, with 58.5% higher token throughput in MTP version for single-user scenarios
## 📊 Performance Metrics
### HealthBench Scores
| Model Name | HealthBench | HealthBench-Hard | HealthBench-Consensus |
|------------|-------------|------------------|-----------------------|
| Baichuan-M2 | 60.1 | 34.7 | 91.5 |
| gpt-oss-120b | 57.6 | 30 | 90 |
| Qwen3-235B-A22B-Thinking-2507 | 55.2 | 25.9 | 90.6 |
| Deepseek-R1-0528 | 53.6 | 22.6 | 91.5 |
| GLM-4.5 | 47.8 | 18.7 | 85.3 |
| Kimi-K2 | 43 | 10.7 | 90.9 |
| gpt-oss-20b | 42.5 | 10.8 | 82.6 |
### General Performance
| Benchmark | Baichuan-M2-32B | Qwen3-32B (Thinking) |
|-----------|-----------------|-----------|
| AIME24 | 83.4 | 81.4 |
| AIME25 | 72.9 | 72.9 |
| Arena-Hard-v2.0 | 45.8 | 44.5 |
| CFBench | 77.6 | 75.7 |
| WritingBench | 8.56 | 7.90 |
*Note: AIME uses max_tokens=64k, others use 32k; temperature=0.6 for all tests.*
## 🔧 Technical Features
📗 **Technical Blog**: [Blog - Baichuan-M2](https://www.baichuan-ai.com/blog/baichuan-M2)
### Large Verifier System
- **Patient Simulator**: Virtual patient system based on real clinical cases
- **Multi-Dimensional Verification**: 8 dimensions including medical accuracy, response completeness, and follow-up awareness
- **Dynamic Scoring**: Real-time generation of adaptive evaluation criteria for complex clinical scenarios
### Medical Domain Adaptation
- **Mid-Training**: Medical knowledge injection while preserving general capabilities
- **Reinforcement Learning**: Multi-stage RL strategy optimization
- **General-Specialized Balance**: Carefully balanced medical, general, and mathematical composite training data
## ⚙️ Quick Start
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.9.0` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path baichuan-inc/Baichuan-M2-32B-GPTQ-Int4 --reasoning-parser qwen3
```
To turn on kv cache FP8 quantization:
```shell
python -m sglang.launch_server --model-path baichuan-inc/Baichuan-M2-32B-GPTQ-Int4 --reasoning-parser qwen3 --kv-cache-dtype fp8_e4m3 --attention-backend flashinfer
```
- vLLM:
```shell
vllm serve baichuan-inc/Baichuan-M2-32B-GPTQ-Int4 --reasoning-parser qwen3
```
To turn on kv cache FP8 quantization:
```shell
vllm serve baichuan-inc/Baichuan-M2-32B-GPTQ-Int4 --reasoning-parser qwen3 --kv_cache_dtype fp8_e4m3
```
## MTP inference with SGLang
1. Replace the qwen2.py file in the sglang installation directory with draft/qwen2.py.
2. Launch sglang:
```
python3 -m sglang.launch_server \
--model Baichuan-M2-32B-GPTQ-Int4 \
--speculative-algorithm EAGLE3 \
--speculative-draft-model-path Baichuan-M2-32B-GPTQ-Int4/draft \
--speculative-num-steps 6 \
--speculative-eagle-topk 10 \
--speculative-num-draft-tokens 32 \
--mem-fraction 0.9 \
--cuda-graph-max-bs 2 \
--reasoning-parser qwen3 \
--dtype bfloat16
```
## ⚠️ Usage Notices
1. **Medical Disclaimer**: For research and reference only; cannot replace professional medical diagnosis or treatment
2. **Intended Use Cases**: Medical education, health consultation, clinical decision support
3. **Safe Use**: Recommended under guidance of medical professionals
## 📄 License
Licensed under the [Apache License 2.0](LICENSE). Research and commercial use permitted.
## 🤝 Acknowledgements
- Base Model: Qwen2.5-32B
- Training Framework: verl
- Inference Engines: vLLM, SGLang
- Quantization: AutoRound, GPTQ
Thank you to the open-source community. We commit to continuous contribution and advancement of healthcare AI.
## 📞 Contact Us
- Resources: [Baichuan AI Website](https://www.baichuan-ai.com)
- Technical Support: [GitHub](https://github.com/baichuan-inc)
---
<div align="center">
**Empowering Healthcare with AI, Making Health Accessible to All**
</div>
|
irfananjum/my-own-model
|
irfananjum
| 2025-08-11T06:50:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T06:49:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
OceanOmics/eDNABERT-S_16S
|
OceanOmics
| 2025-08-11T06:50:04Z | 0 | 0 | null |
[
"pytorch",
"bert",
"text-generation",
"custom_code",
"arxiv:2507.09080",
"arxiv:1910.09700",
"base_model:zhihan1996/DNABERT-S",
"base_model:finetune:zhihan1996/DNABERT-S",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-11T06:11:46Z |
---
license: apache-2.0
base_model:
- zhihan1996/DNABERT-S
pipeline_tag: text-generation
---
# Model Card for eDNABERT-S_16S
## Model Details
### Model Description
This model is our first step towards ecosystem-level modeling.
We finetuned DNABERT-S using all of our eDNA ASVs from the Australian Marine Parks project. We used 36,346 Berry 16S ASVs collected from more than 6,000 samples for finetuning.
A partner model for Miya 12S data is [also available](https://huggingface.co/OceanOmics/eDNABERT-S_12S/).
- **Developed by:** OceanOmics team, Philipp Bayer
- **Funded by** Minderoo Foundation
- **Model type:** BERT
- **Language(s) (NLP):** DNA
- **License:** Apache 2.0
- **Finetuned from model:** DNABERT-S
## Uses
### Installation
There's a conda environment in this repository in DNABERT_S.yml.
```
conda env create -f DNABERT_S.yml
```
### Direct Use
I've been using this model to visualise ecosystem embeddings.
```
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from Bio import SeqIO
from sklearn.manifold import TSNE
import numpy as np
from tqdm import tqdm # For progress tracking
# Device configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
config = AutoConfig.from_pretrained('zhihan1996/DNABERT-S', trust_remote_code = True)
# Load model and tokenizer
tokenizer_16S = AutoTokenizer.from_pretrained('OceanOmics/eDNABERT-S_16S', trust_remote_code=True)
model_16S = AutoModel.from_pretrained('OceanOmics/eDNABERT-S_16S', trust_remote_code=True, config=config)
model_16S.to(device)
model_16S.eval()
names_12, seqs_12 = [], []
for seq in SeqIO.parse('16S_all_ASVs.fasta', 'fasta'):
seqs_12.append(str(seq.seq))
names_12.append(str(seq.id))
print(f"Processing {len(seqs_12)} sequences")
# Load model and tokenizer
tokenizer_16S = AutoTokenizer.from_pretrained('OceanOmics/eDNABERT-S_16S', trust_remote_code=True)
model_16S = AutoModel.from_pretrained('OceanOmics/eDNABERT-S_16S', trust_remote_code=True, config=config)
model_16S.to(device)
model_16S.eval()
names_16, seqs_16 = [], []
for seq in SeqIO.parse('16S_all_ASVs.fasta', 'fasta'):
if 165 <= len(str(seq.seq)) <= 180: # More efficient condition check
seqs_16.append(str(seq.seq))
names_16.append(str(seq.id))
print(f"Processing {len(seqs_16)} sequences")
batch_size = 32 # tested on an A100
num_sequences = len(seqs_16)
all_e_16 = np.zeros((num_sequences, 768))
with torch.no_grad(): # no gradient calculation for inference
for i in tqdm(range(0, num_sequences, batch_size)):
batch_seqs = seqs_16[i:i+batch_size]
inputs = tokenizer_16S(batch_seqs, return_tensors='pt', padding=True)
inputs = {k: v.to(device) for k, v in inputs.items()} # Move inputs to device
hidden_states = model_16S(**inputs)[0]
for j, hidden_state in enumerate(hidden_states):
embedding_mean = torch.mean(hidden_state, dim=0)
all_e_16[i+j] = embedding_mean.cpu().numpy() # Store directly in pre-allocated array
print("Running TSNE...")
X_embedded = TSNE(
n_components=2,
learning_rate='auto',
init='random',
perplexity=50, # Reasonable value
n_jobs=-1 # Use all available cores
).fit_transform(all_e_16)
print("Saving results...")
with open('odr_all_tsne_16S.optimized.tsv', 'w') as out:
for a, name in zip(X_embedded, names_16):
out.write('\t'.join(map(str, list(a) + name.split("XXX"))) + '\n')
```
You can see results visualised in action here: https://marine-parks.minderoo.org/#!/unknown
### Downstream Use
I'm hoping you'll come up with these! It would be great if we could plug this or similar models into ecosystem-level models such as [BioAnalyst](https://arxiv.org/abs/2507.09080)
### Risks and out-of-Scope Use
This model is trained using Berry et al. 16S metabarcoding results based on Australian marine samples. The 16S assay is fairly fish-specific, with some other vertebrate hits such as dolphins, so you might not have the best time applying this model to other organisms or ecosystems.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing
We had many primer dimers in our ASV data, so I got rid of too-short and too-long ASVs. Cehck what your ASVs look like.
#### Training Hyperparameters
Using the original DNABERT-S training script:
```
python main.py [.. skipping data flags..]
--seed 1
--max_length 2000
--train_batch_size 8
--val_batch_size 8
--lr 3e-06
--lr_scale 100
--epochs 3
--feat_dim 128
--temperature 0.05
--con_method same_species
--mix
--mix_alpha 1.0
--mix_layer_num -1
--curriculum
```
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
kristysimon87/gulali-karimi.Original.Video.18.gulali.karimi.viral.video
|
kristysimon87
| 2025-08-11T06:50:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-11T06:49:42Z |
<a href="https://shorturl.at/1rUfR" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Murrad/my-own-model
|
Murrad
| 2025-08-11T06:49:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T06:49:25Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
megafigh/blockassist-bc-deadly_mottled_crow_1754894737
|
megafigh
| 2025-08-11T06:46:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly mottled crow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:46:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly mottled crow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Javierd009/prueba
|
Javierd009
| 2025-08-11T06:45:15Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-10T06:11:41Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ganocafe
---
# Prueba
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ganocafe` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ganocafe",
"lora_weights": "https://huggingface.co/Javierd009/prueba/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Javierd009/prueba', weight_name='lora.safetensors')
image = pipeline('ganocafe').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2306
- Learning rate: 0.0004
- LoRA rank: 24
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Javierd009/prueba/discussions) to add images that show off what you’ve made with this LoRA.
|
salamatmir/my-own-model
|
salamatmir
| 2025-08-11T06:44:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T06:43:44Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Hassan-07-code/my-own-model
|
Hassan-07-code
| 2025-08-11T06:43:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-11T06:43:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754894444
|
IvanJAjebu
| 2025-08-11T06:41:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:41:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sii-research/DigitalGene-7B
|
sii-research
| 2025-08-11T06:40:17Z | 0 | 0 | null |
[
"safetensors",
"qwen2_5_vl",
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T06:22:45Z |
---
license: apache-2.0
---
|
roeker/blockassist-bc-quick_wiry_owl_1754894353
|
roeker
| 2025-08-11T06:40:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:40:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
olivepicker/convnext_tiny.in12k_ft_in1k
|
olivepicker
| 2025-08-11T06:39:27Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-11T06:38:47Z |
---
license: apache-2.0
---
|
wahyuda110/blockassist-bc-stalking_stocky_buffalo_1754894230
|
wahyuda110
| 2025-08-11T06:38:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stalking stocky buffalo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-11T06:38:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stalking stocky buffalo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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